FINAL INTERVIEW QUESTIONS ( ETL - INFORMATICA)

Data warehousing Basics

1. Definition of data warehousing?

ü   Data warehouse is a Subject oriented, Integrated, Time variant, Non volatile collection of data in support of management's decision making process.

                                                                                                                        

Subject Oriented

Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case makes the data warehouse subject oriented.

Integrated

Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.

Nonvolatile

Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

Time Variant

In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse's focus on change over time is what is meant by the term time variant.

2. How many stages in Datawarehousing?

Data warehouse generally includes two stages

ü   ETL

ü   Report Generation

ETL

Short for extract, transform, load, three database functions that are combined into one tool

• Extract -- the process of reading data from a source database.
• Transform -- the process of converting the extracted data from its previous form into required form
• Load -- the process of writing the data into the target database.

ETL is used to migrate data from one database to another, to form data marts and data warehouses and also to convert databases from one format to another format.

It is used to retrieve the data from various operational databases and is transformed into useful information and finally loaded into Datawarehousing system.

1 INFORMATICA

2 ABINITO

3 DATASTAGE

4. BODI

5 ORACLE WAREHOUSE BUILDERS

Report generation

          In report generation, OLAP is used (i.e.) online analytical processing. It is a set of specification which allows the client applications in retrieving the data for analytical processing.

It is a specialized tool that sits between a database and user in order to provide various analyses of the data stored in the database.

OLAP Tool is a reporting tool which generates the reports that are useful for Decision support for top level management.

1. Business Objects
2. Cognos
3. Micro strategy
4. Hyperion
5. Oracle Express

6.    Microsoft Analysis Services

 

• Different Between OLTP and OLAP

	OLTP	OLAP
1	Application Oriented (e.g., purchase order it is functionality of an application)	Subject Oriented (subject in the sense customer, product, item, time)
2	Used to run business	Used to analyze business
3	Detailed data	Summarized data
4	Repetitive access	Ad-hoc access
5	Few Records accessed at a time (tens), simple query	Large volumes accessed at a time(millions), complex query
6	Small database	Large Database
7	Current data	Historical data
8	Clerical User	Knowledge User
9	Row by Row Loading	Bulk Loading
10	Time invariant	Time variant
11	Normalized data	De-normalized data
12	E – R schema	Star schema

3. What are the types of datawarehousing?

EDW (Enterprise datawarehousing)

ü   It provides a central database for decision support throughout the enterprise

ü   It is a collection of DATAMARTS

DATAMART

ü   It is a subset of Datawarehousing

ü   It is a subject oriented database which supports the needs of individuals depts. in an organizations

ü   It is called high performance query structure

ü   It supports particular line of business like sales, marketing etc..

ODS (Operational data store)

ü   It is defined as an integrated view of operational database designed to support operational monitoring

ü   It is a collection of operational data sources designed to support Transaction processing

ü   Data is refreshed near real-time and used for business activity

ü   It is an intermediate between the OLTP and OLAP which helps to create an instance reports

4.    What are the modeling involved in Data Warehouse Architecture?

5.    What are the types of Approach in DWH?

Bottom up approach: first we need to develop data mart then we integrate these data mart into EDW

Top down approach: first we need to develop EDW then form that EDW we develop data mart

Bottom up

OLTP             ETL                Data mart                   DWH        OLAP

Top down

OLTP             ETL                DWH                Data mart             OLAP

Top down

ü   Cost of initial planning & design is high

ü   Takes longer duration of more than an year

Bottom up

ü   Planning & Designing the Data Marts without waiting for the Global warehouse design

ü   Immediate results from the data marts

ü   Tends to take less time to implement

ü   Errors in critical modules are detected earlier.

ü   Benefits are realized in the early phases.

ü   It is a Best Approach

Data Modeling Types:

ü   Conceptual Data Modeling

ü   Logical Data Modeling

ü   Physical Data Modeling

ü   Dimensional Data Modeling

1. Conceptual Data Modeling

ü   Conceptual data model includes all major entities and relationships and does not contain much detailed level of information about attributes and is often used in the INITIAL PLANNING PHASE

ü   Conceptual data model is created by gathering business requirements from various sources like business documents, discussion with functional teams, business analysts, smart management experts and end users who do the reporting on the database. Data modelers create conceptual data model and forward that model to functional team for their review.

ü   Conceptual data modeling gives an idea to the functional and technical team about how business requirements would be projected in the logical data model.

2. Logical Data Modeling

ü   This is the actual implementation and extension of a conceptual data model. Logical data model includes all required entities, attributes, key groups, and relationships that represent business information and define business rules.

3. Physical Data Modeling

ü   Physical data model includes all required tables, columns, relationships, database properties for the physical implementation of databases. Database performance, indexing strategy, physical storage and demoralization are important parameters of a physical model.

Logical vs. Physical Data Modeling

Logical Data Model	Physical Data Model
Represents business information and defines business rules	Represents the physical implementation of the model in a database.
Entity	Table
Attribute	Column
Primary Key	Primary Key Constraint
Alternate Key	Unique Constraint or Unique Index
Inversion Key Entry	Non Unique Index
Rule	Check Constraint, Default Value
Relationship	Foreign Key
Definition	Comment

Dimensional Data Modeling

ü   Dimension model consists of fact and dimension tables

ü   It is an approach to develop the schema DB designs

Types of Dimensional modeling

ü   Star schema

ü   Snow flake schema

ü   Star flake schema (or) Hybrid schema

ü   Multi star schema

What is Star Schema?

ü   The Star Schema Logical database design which contains a centrally located fact table surrounded by at least one or more dimension tables

ü   Since the database design looks like a star, hence it is called star schema db

ü   The Dimension table contains Primary keys and the textual descriptions

ü   It contain de-normalized business information

ü   A Fact table contains a composite key and measures

ü   The measure are of types of key performance indicators which are used to evaluate the enterprise performance in the form of success and failure

ü   Eg: Total revenue , Product sale , Discount given, no of customers

ü   To generate meaningful report the report should contain at least one dimension and one fact table

The advantage of star schema

ü   Less number of joins

ü   Improve query performance

ü   Slicing down

ü   Easy understanding of data.

Disadvantage:

ü    Require more storage space

Example of Star Schema:

Snowflake Schema

ü   In star schema, If the dimension tables are spitted into one or more dimension tables

ü   The de-normalized dimension tables are spitted into a normalized dimension table

Example of Snowflake Schema:

ü   In Snowflake schema, the example diagram shown below has 4 dimension tables, 4 lookup tables and 1 fact table. The reason is that hierarchies (category, branch, state, and month) are being broken out of the dimension tables (PRODUCT, ORGANIZATION, LOCATION, and TIME) respectively and separately.

ü   It increases the number of joins and poor performance in retrieval of data.

ü   In few organizations, they try to normalize the dimension tables to save space.

ü   Since dimension tables hold less space snow flake schema approach may be avoided.

ü   Bit map indexes cannot be effectively utilized

Important aspects of Star Schema & Snow Flake Schema

ü   In a star schema every dimension will have a primary key.

ü   In a star schema, a dimension table will not have any parent table.

ü   Whereas in a snow flake schema, a dimension table will have one or more parent tables.

ü   Hierarchies for the dimensions are stored in the dimensional table itself in star schema.

ü   Whereas hierarchies are broken into separate tables in snow flake schema. These hierarchies help to drill down the data from topmost hierarchies to the lowermost hierarchies.

Star flake schema (or) Hybrid Schema

ü   Hybrid schema is a combination of Star and Snowflake schema

Multi Star schema

ü   Multiple fact tables sharing a set of dimension tables

ü   Confirmed Dimensions are nothing but Reusable Dimensions.

ü   The dimensions which u r using multiple times or in multiple data marts.

ü   Those are common in different data marts

Measure Types (or) Types of Facts

• Additive - Measures that can be summed up across all dimensions.
• Ex: Sales Revenue
• Semi Additive - Measures that can be summed up across few dimensions and not with others
• Ex: Current Balance
• Non Additive - Measures that cannot be summed up across any of the dimensions.
• Ex: Student attendance

Surrogate Key

ü   Joins between fact and dimension tables should be based on surrogate keys

ü   Users should not obtain any information by looking at these keys

ü   These keys should be simple integers

A sample data warehouse schema

Why need staging area for DWH?

ü   Staging area needs to clean operational data before loading into data warehouse.

ü   Cleaning in the sense your merging data which comes from different source.

ü   It’s the area where most of the ETL is done

Data Cleansing

ü   It is used  to remove duplications

ü   It is used to correct wrong email addresses

ü   It is used to identify missing data

ü   It used to convert the data types

ü   It is used to capitalize name & addresses.

Types of Dimensions:

There are three types of Dimensions

ü   Confirmed Dimensions

ü   Junk Dimensions Garbage Dimension

ü   Degenerative Dimensions

ü   Slowly changing Dimensions

Garbage Dimension or Junk Dimension

ü   Confirmed is something which can be shared by multiple Fact Tables or multiple Data Marts.

ü   Junk Dimensions is grouping flagged values

ü   Degenerative Dimension is something dimensional in nature but exist fact table.(Invoice No)

            Which is neither fact nor strictly dimension attributes. These are useful for some kind of analysis. These are kept as attributes in fact table called degenerated dimension

Degenerate dimension: A column of the key section of the fact table that does not have the associated dimension table but used for reporting and analysis, such column is called degenerate dimension or line item dimension.

For ex, we have a fact table with customer_id, product_id, branch_id, employee_id, bill_no, and date in key section and price, quantity, amount in measure section. In this fact table, bill_no from key section is a single value; it has no associated dimension table. Instead of creating a 

Separate dimension table for that single value, we can Include it in fact table to improve performance. SO here the column, bill_no is a degenerate dimension or line item dimension.

Informatica Architecture

The Power Center domain

            It is a primary unit of the Administration.

            Can have single and multiple domains.

            It is a collection of nodes and services.

Nodes

   A node is the logical representation of a machine in a domain

One node in the domain acts as a gateway node to receive service requests from clients and route them to the appropriate service and node

Integration Service:

Integration Service does all the real job. It extracts data from sources, processes it as   per the business logic and loads data to targets.

Repository Service:

Repository Service is used to fetch the data from the repository and sends it back to the     requesting components (mostly client tools and integration service)

Power Center Repository:

Repository is nothing but a relational database which stores all the metadata created in  Power Center.

Power Center Client Tools:

 The Power Center Client consists of multiple tools.

Power Center Administration Console:

This is simply a web-based administration tool you can use to administer the Power Center installation.

Q. How can you define a transformation? What are different types of transformations available in Informatica?

A. A transformation is a repository object that generates, modifies, or passes data. The Designer provides a set of transformations that perform specific functions. For example, an Aggregator transformation performs calculations on groups of data. Below are the various transformations available in Informatica:

• Aggregator

• Custom

• Expression

• External Procedure

• Filter

• Input

• Joiner

• Lookup

• Normalizer

• Rank

• Router

• Sequence Generator

• Sorter

• Source Qualifier

• Stored Procedure

• Transaction Control

• Union

• Update Strategy

• XML Generator

• XML Parser

• XML Source Qualifier

Q. What is a source qualifier? What is meant by Query Override?

A. Source Qualifier represents the rows that the PowerCenter Server reads from a relational or flat file source when it runs a session. When a relational or a flat file source definition is added to a mapping, it is connected to a Source Qualifier transformation.

PowerCenter Server generates a query for each Source Qualifier Transformation whenever it runs the session. The default query is SELET statement containing all the source columns. Source Qualifier has capability to override this default query by changing the default settings of the transformation properties. The list of selected ports or the order they appear in the default query should not be changed in overridden query.

Q. What is aggregator transformation?

A. The Aggregator transformation allows performing aggregate calculations, such as averages and sums. Unlike Expression Transformation, the Aggregator transformation can only be used to perform calculations on groups. The Expression transformation permits calculations on a rowby-row basis only.

Aggregator Transformation contains group by ports that indicate how to group the data. While grouping the data, the aggregator transformation outputs the last row of each group unless otherwise specified in the transformation properties.

Various group by functions available in Informatica are : AVG, COUNT, FIRST, LAST, MAX, MEDIAN, MIN, PERCENTILE, STDDEV, SUM, VARIANCE.

Q. What is Incremental Aggregation?

A. Whenever a session is created for a mapping Aggregate Transformation, the session option for Incremental Aggregation can be enabled. When PowerCenter performs incremental aggregation, it passes new source data through the mapping and uses historical cache data to perform new aggregation calculations incrementally.

Q. How Union Transformation is used?

A. The union transformation is a multiple input group transformation that can be used to merge data from various sources (or pipelines). This transformation works just like UNION ALL statement in SQL, that is used to combine result set of two SELECT statements.

Q. Can two flat files be joined with Joiner Transformation?

A. Yes, joiner transformation can be used to join data from two flat file sources.

Q. What is a look up transformation?

A. This transformation is used to lookup data in a flat file or a relational table, view or synonym. It compares lookup transformation ports (input ports) to the source column values based on the lookup condition. Later returned values can be passed to other transformations.

Q. Can a lookup be done on Flat Files?

A. Yes.

Q. What is a mapplet?

A. A mapplet is a reusable object that is created using mapplet designer. The mapplet contains set of transformations and it allows us to reuse that transformation logic in multiple mappings.

Q. What does reusable transformation mean?

A. Reusable transformations can be used multiple times in a mapping. The reusable

transformation is stored as a metadata separate from any other mapping that uses the

transformation. Whenever any changes to a reusable transformation are made, all the mappings where the transformation is used will be invalidated.

Q. What is update strategy and what are the options for update strategy?

A. Informatica processes the source data row-by-row. By default every row is marked to be inserted in the target table. If the row has to be updated/inserted based on some logic Update Strategy transformation is used. The condition can be specified in Update Strategy to mark the processed row for update or insert.

Following options are available for update strategy:

• DD_INSERT: If this is used the Update Strategy flags the row for insertion. Equivalent numeric value of DD_INSERT is 0.

• DD_UPDATE: If this is used the Update Strategy flags the row for update. Equivalent numeric value of DD_UPDATE is 1.

• DD_DELETE: If this is used the Update Strategy flags the row for deletion. Equivalent numeric value of DD_DELETE is 2.

• DD_REJECT: If this is used the Update Strategy flags the row for rejection. Equivalent numeric value of DD_REJECT is 3.

 

Q. What are the types of loading in Informatica?

There are two types of loading, 1. Normal loading and 2. Bulk loading.

In normal loading, it loads record by record and writes log for that. It takes comparatively a longer time to load data to the target.

In bulk loading, it loads number of records at a time to target database. It takes less time to load data to target.

Q. What is aggregate cache in aggregator transformation? 

The aggregator stores data in the aggregate cache until it completes aggregate calculations. When you run a session that uses an aggregator transformation, the informatica server creates index and data caches in memory to process the transformation. If the informatica server requires more space, it stores overflow values in cache files. 

Q. What type of repositories can be created using Informatica Repository Manager?

A. Informatica PowerCenter includes following type of repositories:

• Standalone Repository: A repository that functions individually and this is unrelated to any other repositories.

• Global Repository: This is a centralized repository in a domain. This repository can

contain shared objects across the repositories in a domain. The objects are shared through global shortcuts.

• Local Repository: Local repository is within a domain and it��s not a global repository. Local repository can connect to a global repository using global shortcuts and can use objects in its shared folders.

• Versioned Repository: This can either be local or global repository but it allows version control for the repository. A versioned repository can store multiple copies, or versions of an object. This feature allows efficiently developing, testing and deploying metadata in the production environment.

Q. What is a code page?

A. A code page contains encoding to specify characters in a set of one or more languages. The code page is selected based on source of the data. For example if source contains Japanese text then the code page should be selected to support Japanese text.

When a code page is chosen, the program or application for which the code page is set, refers to a specific set of data that describes the characters the application recognizes. This influences the way that application stores, receives, and sends character data.

Q. Which all databases PowerCenter Server on Windows can connect to?

A. PowerCenter Server on Windows can connect to following databases:

• IBM DB2

• Informix

• Microsoft Access

• Microsoft Excel

• Microsoft SQL Server

• Oracle

• Sybase

• Teradata

Q. Which all databases PowerCenter Server on UNIX can connect to?

A. PowerCenter Server on UNIX can connect to following databases:

• IBM DB2

• Informix

• Oracle

• Sybase

• Teradata

Q. How to execute PL/SQL script from Informatica mapping?

A. Stored Procedure (SP) transformation can be used to execute PL/SQL Scripts. In SP

Transformation PL/SQL procedure name can be specified. Whenever the session is executed, the session will call the pl/sql procedure.

Q. What is Data Driven?

The informatica server follows instructions coded into update strategy transformations within the session mapping which determine how to flag records for insert, update, delete or reject. If we do not choose data driven option setting, the informatica server ignores all update strategy transformations in the mapping.

Q. What are the types of mapping wizards that are provided in Informatica?

The designer provide two mapping wizard.

1. Getting Started Wizard - Creates mapping to load static facts and dimension tables as well as slowly growing dimension tables.

2. Slowly Changing Dimensions Wizard - Creates mappings to load slowly changing dimension tables based on the amount of historical dimension data we want to keep and the method we choose to handle historical dimension data.

Q. What is Load Manager?

A. While running a Workflow, the PowerCenter Server uses the Load Manager

process and the Data Transformation Manager Process (DTM) to run the workflow and carry out workflow tasks. When the PowerCenter Server runs a workflow, the Load Manager performs the following tasks:

1. Locks the workflow and reads workflow properties.

2. Reads the parameter file and expands workflow variables.

3. Creates the workflow log file.

4. Runs workflow tasks.

5. Distributes sessions to worker servers.

6. Starts the DTM to run sessions.

7. Runs sessions from master servers.

8. Sends post-session email if the DTM terminates abnormally.

When the PowerCenter Server runs a session, the DTM performs the following tasks:

1. Fetches session and mapping metadata from the repository.

2. Creates and expands session variables.

3. Creates the session log file.

4. Validates session code pages if data code page validation is enabled. Checks

Query conversions if data code page validation is disabled.

5. Verifies connection object permissions.

6. Runs pre-session shell commands.

7. Runs pre-session stored procedures and SQL.

8. Creates and runs mappings, reader, writer, and transformation threads to extract,

transform, and load data.

9. Runs post-session stored procedures and SQL.

10. Runs post-session shell commands.

11. Sends post-session email.

Q. What is Data Transformation Manager?

A. After the load manager performs validations for the session, it creates the DTM

process. The DTM process is the second process associated with the session run. The

primary purpose of the DTM process is to create and manage threads that carry out

the session tasks.

• The DTM allocates process memory for the session and divide it into buffers. This

is also known as buffer memory. It creates the main thread, which is called the

master thread. The master thread creates and manages all other threads.

• If we partition a session, the DTM creates a set of threads for each partition to

allow concurrent processing.. When Informatica server writes messages to the

session log it includes thread type and thread ID.

Following are the types of threads that DTM creates:

Master Thread - Main thread of the DTM process. Creates and manages all other

threads.

Mapping Thread - One Thread to Each Session. Fetches Session and Mapping

Information.

Pre and Post Session Thread - One Thread each to Perform Pre and Post Session

Operations.

Reader Thread - One Thread for Each Partition for Each Source Pipeline.

Writer Thread - One Thread for Each Partition if target exist in the source pipeline

write to the target.

Transformation Thread - One or More Transformation Thread For Each Partition.

Q. What is Session and Batches?

Session - A Session Is A set of instructions that tells the Informatica Server How

And When To Move Data From Sources To Targets. After creating the session, we

can use either the server manager or the command line program pmcmd to start

or stop the session.

Batches - It Provides A Way to Group Sessions For Either Serial Or Parallel Execution By The Informatica Server. There Are Two Types Of Batches:

1. Sequential - Run Session One after the Other.

2. Concurrent - Run Session At The Same Time.

Q. How many ways you can update a relational source definition and what

are they?

A. Two ways

1. Edit the definition

2. Reimport the definition

Q. What is a transformation?

A. It is a repository object that generates, modifies or passes data.

Q. What are the designer tools for creating transformations?

A. Mapping designer

Transformation developer

Mapplet designer

Q. In how many ways can you create ports?

A. Two ways

1. Drag the port from another transformation

2. Click the add button on the ports tab.

Q. What are reusable transformations?

A. A transformation that can be reused is called a reusable transformation

They can be created using two methods:

1. Using transformation developer

2. Create normal one and promote it to reusable

Q. Is aggregate cache in aggregator transformation?

A. The aggregator stores data in the aggregate cache until it completes aggregate calculations. When u run a session that uses an aggregator transformation, the Informatica server creates index and data caches in memory to process the transformation. If the Informatica server requires more space, it stores overflow values in cache files.

Q. What r the settings that u use to configure the joiner transformation?

·         Master and detail source

·         Type of join

·         Condition of the join

Q. What are the join types in joiner transformation?

A. Normal (Default) -- only matching rows from both master and detail

Master outer -- all detail rows and only matching rows from master

Detail outer -- all master rows and only matching rows from detail

Full outer -- all rows from both master and detail (matching or non matching)

Q. What are the joiner caches?

A. When a Joiner transformation occurs in a session, the Informatica Server reads all the records from the master source and builds index and data caches based on the master rows. After building the caches, the Joiner transformation reads records

from the detail source and performs joins.

Q. What r the types of lookup caches?

·         Static cache: You can configure a static or read-only cache for only lookup table. By default Informatica server creates a static cache. It caches the lookup table and lookup values in the cache for each row that comes into the transformation. When the lookup condition is true, the Informatica server does not update the cache while it processes the lookup transformation.

·         Dynamic cache: If you want to cache the target table and insert new rows into cache and the target, you can create a look up transformation to use dynamic cache. The Informatica server dynamically inserts data to the target table.

·         Persistent cache: You can save the lookup cache files and reuse them the next time the Informatica server processes a lookup transformation configured to use the cache.

·         Recache from database: If the persistent cache is not synchronized with the lookup table, you can configure the lookup transformation to rebuild the lookup cache.

·         Shared cache: You can share the lookup cache between multiple transactions. You can share unnamed cache between transformations in the same mapping.

Q. What is Transformation?

A: Transformation is a repository object that generates, modifies, or passes data.

Transformation performs specific function. They are two types of transformations:

1. Active

Rows, which are affected during the transformation or can change the no of rows that pass through it. Eg: Aggregator, Filter, Joiner, Normalizer, Rank, Router, Source qualifier, Update Strategy, ERP Source Qualifier, Advance External Procedure.

2. Passive

Does not change the number of rows that pass through it. Eg: Expression, External Procedure, Input, Lookup, Stored Procedure, Output, Sequence Generator, XML Source Qualifier.

Q. What are Options/Type to run a Stored Procedure?

A: Normal: During a session, the stored procedure runs where the

transformation exists in the mapping on a row-by-row basis. This is useful for calling the stored procedure for each row of data that passes through the mapping, such as running a calculation against an input port. Connected stored procedures run only in normal mode.

Pre-load of the Source. Before the session retrieves data from the source, the stored procedure runs. This is useful for verifying the existence of tables or performing joins of data in a temporary table.

Post-load of the Source. After the session retrieves data from the source, the stored procedure runs. This is useful for removing temporary tables.

Pre-load of the Target. Before the session sends data to the target, the stored procedure runs. This is useful for verifying target tables or disk space on the target system.

Post-load of the Target. After the session sends data to the target, the stored procedure runs. This is useful for re-creating indexes on the database. It must contain at least one Input and one Output port.

Q. What kinds of sources and of targets can be used in Informatica?

·         Sources may be Flat file, relational db or XML.

·         Target may be relational tables, XML or flat files.

Q: What is Session Process?

A: The Load Manager process. Starts the session, creates the DTM process, and

sends post-session email when the session completes.

Q. What is DTM process?

A: The DTM process creates threads to initialize the session, read, write, transform

data and handle pre and post-session operations.

Q. What is the different type of tracing levels?

Tracing level represents the amount of information that Informatica Server writes in a log file. Tracing levels store information about mapping and transformations. There are 4 types of tracing levels supported

1. Normal: It specifies the initialization and status information and summarization of the success rows and target rows and the information about the skipped rows due to transformation errors.

2. Terse: Specifies Normal + Notification of data

3. Verbose Initialization: In addition to the Normal tracing, specifies the location of the data cache files and index cache files that are treated and detailed transformation statistics for each and every transformation within the mapping.

4. Verbose Data: Along with verbose initialization records each and every record processed by the informatica server.

Q. Types of Dimensions?

A dimension table consists of the attributes about the facts. Dimensions store the textual descriptions of the business.

Conformed Dimension:

Conformed dimensions mean the exact same thing with every possible fact table to which they are joined.

Eg: The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts.

Junk Dimension:

A junk dimension is a collection of random transactional codes flags and/or text attributes that are unrelated to any particular dimension. The junk dimension is simply a structure that provides a convenient place to store the junk attributes.

Eg: Assume that we have a gender dimension and marital status dimension. In the fact table we need to maintain two keys referring to these dimensions. Instead of that create a junk dimension which has all the combinations of gender and marital status (cross join gender and marital status table and create a junk table). Now we can maintain only one key in the fact table.

Degenerated Dimension:

A degenerate dimension is a dimension which is derived from the fact table and doesn’t have its own dimension table.

Eg: A transactional code in a fact table.

Slowly changing dimension:

Slowly changing dimensions are dimension tables that have slowly increasing

data as well as updates to existing data.

Q. What are the output files that the Informatica server creates during the

session running?

Informatica server log: Informatica server (on UNIX) creates a log for all status and

error messages (default name: pm.server.log). It also creates an error log for error

messages. These files will be created in Informatica home directory

Session log file: Informatica server creates session log file for each session. It writes

information about session into log files such as initialization process, creation of sql

commands for reader and writer threads, errors encountered and load summary. The

amount of detail in session log file depends on the tracing level that you set.

Session detail file: This file contains load statistics for each target in mapping.

Session detail includes information such as table name, number of rows written or

rejected. You can view this file by double clicking on the session in monitor window.

Performance detail file: This file contains information known as session performance

details which helps you where performance can be improved. To generate this file

select the performance detail option in the session property sheet.

Reject file: This file contains the rows of data that the writer does not write to

targets.

Control file: Informatica server creates control file and a target file when you run a

session that uses the external loader. The control file contains the information about

the target flat file such as data format and loading instructions for the external

loader.

Post session email: Post session email allows you to automatically communicate

information about a session run to designated recipients. You can create two

different messages. One if the session completed successfully the other if the session

fails.

Indicator file: If you use the flat file as a target, you can configure the Informatica

server to create indicator file. For each target row, the indicator file contains a

number to indicate whether the row was marked for insert, update, delete or reject.

Output file: If session writes to a target file, the Informatica server creates the

target file based on file properties entered in the session property sheet.

Cache files: When the Informatica server creates memory cache it also creates cache

files.

For the following circumstances Informatica server creates index and data cache

files:

Aggregator transformation

Joiner transformation

Rank transformation

Lookup transformation

Q. What is meant by lookup caches?

A. The Informatica server builds a cache in memory when it processes the first row

of a data in a cached look up transformation. It allocates memory for the cache

based on the amount you configure in the transformation or session properties. The

Informatica server stores condition values in the index cache and output values in

the data cache.

Q. How do you identify existing rows of data in the target table using lookup

transformation?

A. There are two ways to lookup the target table to verify a row exists or not :

1. Use connect dynamic cache lookup and then check the values of NewLookuprow

Output port to decide whether the incoming record already exists in the table / cache

or not.

2. Use Unconnected lookup and call it from an expression transformation and check

the Lookup condition port value (Null/ Not Null) to decide whether the incoming

record already exists in the table or not.

Q. What are Aggregate tables?

Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions. Retrieving the required data from the actual table, which have millions of records will take more time and also affects the server performance. To avoid this we can aggregate the table to certain required level and can use it. This tables reduces the load in the database server and increases the performance of the query and can retrieve the result very fastly.

Q. What is a level of Granularity of a fact table?

Level of granularity means level of detail that you put into the fact table in a data warehouse. For example: Based on design you can decide to put the sales data in each transaction. Now, level of granularity would mean what detail you are willing to put for each transactional fact. Product sales with respect to each minute or you want to aggregate it upto minute and put that data.

Q. What is session?

A session is a set of instructions to move data from sources to targets.

Q. What is worklet?

Worklet are objects that represent a set of workflow tasks that allow to reuse a set of workflow logic in several window.

Use of Worklet: You can bind many of the tasks in one place so that they can easily get identified and also they can be of a specific purpose.

Q. What is workflow?

A workflow is a set of instructions that tells the Informatica server how to execute the tasks.

Q. Why cannot we use sorted input option for incremental aggregation?

In incremental aggregation, the aggregate calculations are stored in historical cache on the server. In this historical cache the data need not be in sorted order.  If you give sorted input, the records come as presorted for that particular run but in the historical cache the data may not be in the sorted order. That is why this option is not allowed.

Q. What is target load order plan?

You specify the target loadorder based on source qualifiers in a mapping. If you have the multiple source qualifiers connected to the multiple targets, you can designate the order in which informatica server loads data into the targets.

The Target load Plan defines the order in which data extract from source qualifier transformation. In Mappings (tab) – Target Load Order Plan

Q. What is constraint based loading?

Constraint based load order defines the order of loading the data into the multiple targets based on primary and foreign keys constraints.

Set the option is: Double click the session

Configure Object –> check the Constraint Based Loading

Q. What is the status code in stored procedure transformation?

Status code provides error handling for the informatica server during the session. The stored procedure issues a status code that notifies whether or not stored procedure completed successfully. This value cannot see by the user. It only used by the informatica server to determine whether to continue running the session or stop.

Q. Define Informatica Repository?

The Informatica repository is a relational database that stores information, or metadata, used by the Informatica Server and Client tools. Metadata can include information such as mappings describing how to transform source data, sessions indicating when you want the Informatica Server to perform the transformations, and connect strings for sources and targets.

The repository also stores administrative information such as usernames and passwords, permissions and privileges, and product version.

Use repository manager to create the repository. The Repository Manager connects to the repository database and runs the code needed to create the repository tables. These tables stores metadata in specific format the informatica server, client tools use.

Q. What is a metadata?

Designing a data mart involves writing and storing a complex set of instructions. You need to know where to get data (sources), how to change it, and where to write the information (targets). PowerMart and PowerCenter call this set of instructions metadata. Each piece of metadata (for example, the description of a source table in an operational database) can contain comments about it.

In summary, Metadata can include information such as mappings describing how to transform source data, sessions indicating when you want the Informatica Server to perform the transformations, and connect strings for sources and targets.

 

Q. What is metadata reporter?

It is a web based application that enables you to run reports against repository metadata. With a Meta data reporter you can access information about your repository without having knowledge of sql, transformation language or underlying tables in the repository.

Q. What are the types of metadata that stores in repository?

Source definitions. Definitions of database objects (tables, views, synonyms) or files that provide source data. 
Target definitions. Definitions of database objects or files that contain the target data. Multi-dimensional metadata. Target definitions that are configured as cubes and dimensions. 
Mappings. A set of source and target definitions along with transformations containing business logic that you build into the transformation. These are the instructions that the Informatica Server uses to transform and move data. 
Reusable transformations. Transformations that you can use in multiple mappings. 
Mapplets. A set of transformations that you can use in multiple mappings. 
Sessions and workflows. Sessions and workflows store information about how and when the Informatica Server moves data. A workflow is a set of instructions that describes how and when to run tasks related to extracting, transforming, and loading data. A session is a type of task that you can put in a workflow. Each session corresponds to a single mapping.

Following are the types of metadata that stores in the repository

·         Database Connections

·         Global Objects

·         Multidimensional Metadata

·         Reusable Transformations

·         Short cuts

·         Transformations

Q. How can we store previous session logs?

Go to Session Properties –> Config Object –> Log Options

Select the properties as follows….

Save session log by –> SessionRuns

Save session log for these runs –> Change the number that you want to save the number of log files (Default is 0)

If you want to save all of the logfiles created by every run, and then select the option

Save session log for these runs –> Session TimeStamp

You can find these properties in the session/workflow Properties.

Q. What is Changed Data Capture?

Changed Data Capture (CDC) helps identify the data in the source system that has changed since the last extraction. With CDC data extraction takes place at the same time the insert update or delete operations occur in the source tables and the change data is stored inside the database in change tables.

The change data thus captured is then made available to the target systems in a controlled manner.

Q. What is an indicator file? and how it can be used?

Indicator file is used for Event Based Scheduling when you don’t know when the Source Data is available. A shell command, script or a batch file creates and send this indicator file to the directory local to the Informatica Server. Server waits for the indicator file to appear before running the session.

Q. What is audit table? and What are the columns in it?

Audit Table is nothing but the table which contains about your workflow names and session names. It contains information about workflow and session status and their details.

·         WKFL_RUN_ID

·         WKFL_NME

·         START_TMST

·         END_TMST

·         ROW_INSERT_CNT

·         ROW_UPDATE_CNT

·         ROW_DELETE_CNT

·         ROW_REJECT_CNT

Q. If session fails after loading 10000 records in the target, how can we load 10001th record when we run the session in the next time?

Select the Recovery Strategy in session properties as “Resume from the last check point“. Note – Set this property before running the session

Q. Informatica Reject File – How to identify rejection reason

D - Valid data or Good Data. Writer passes it to the target database. The target accepts it unless a database error occurs, such as finding a duplicate key while inserting.

O - Overflowed Numeric Data. Numeric data exceeded the specified precision or scale for the column. Bad data, if you configured the mapping target to reject overflow or truncated data.

N - Null Value. The column contains a null value. Good data. Writer passes it to the target, which rejects it if the target database does not accept null values.

T - Truncated String Data. String data exceeded a specified precision for the column, so the Integration Service truncated it. Bad data, if you configured the mapping target to reject overflow or truncated data.

Also to be noted that the second column contains column indicator flag value ‘D’ which signifies that the Row Indicator is valid.

Now let us see how Data in a Bad File looks like:

0,D,7,D,John,D,5000.375,O,,N,BrickLand Road Singapore,T

Q. What is “Insert Else Update” and “Update Else Insert”?

These options are used when dynamic cache is enabled.

·         Insert Else Update option applies to rows entering the lookup transformation with the row type of insert. When this option is enabled the integration service inserts new rows in the cache and updates existing rows. When disabled, the Integration Service does not update existing rows.

·         Update Else Insert option applies to rows entering the lookup transformation with the row type of update. When this option is enabled, the Integration Service updates existing rows, and inserts a new row if it is new. When disabled, the Integration Service does not insert new rows.

Q. What are the Different methods of loading Dimension tables?

Conventional Load - Before loading the data, all the Table constraints will be checked against the data.

Direct load (Faster Loading) - All the Constraints will be disabled. Data will be loaded directly. Later the data will be checked against the table constraints and the bad data won’t be indexed.

Q. What are the different types of Commit intervals?

The different commit intervals are: 

·         Source-based commit. The Informatica Server commits data based on the number of source rows. The commit point is the commit interval you configure in the session properties.

·         Target-based commit. The Informatica Server commits data based on the number of target rows and the key constraints on the target table. The commit point also depends on the buffer block size and the commit interval. 

Q. How to add source flat file header into target file?

Edit Task-->Mapping-->Target-->Header Options--> Output field names

Q. How to load name of the file into relation target?

Source Definition-->Properties-->Add currently processed file name port

Q. How to return multiple columns through un-connect lookup?

Suppose your look table has f_name,m_name,l_name and you are using unconnected lookup. In override SQL of lookup use f_name||~||m_name||~||l_name you can easily get this value using unconnected lookup in expression. Use substring function in expression transformation to separate these three columns and make then individual port for downstream transformation /Target.

-----------------------------------------------------------------------------------------

Q. What is Factless fact table? In which purpose we are using this in our DWH projects? Plz give me the proper answer?

It is a fact table which does not contain any measurable data.

EX: Student attendance fact (it contains only Boolean values, whether student attended class or not ? Yes or No.)

A Factless fact table contains only the keys but there is no measures or in other way we can say that it contains no facts.  Generally it is used to integrate the fact tables

Factless fact table contains only foreign keys. We can have two kinds of aggregate functions from the factless fact one is count and other is distinct count.

2 purposes of factless fact

1. Coverage: to indicate what did NOT happen. Like to 

Like: which product did not sell well in a particular region?

2. Event tracking: To know if the event took place or not. 

Like: Fact for tracking student’s attendance will not contain any measures.

Q. What is staging area?

Staging area is nothing but to apply our logic to extract the data from source and cleansing the data and put the data into meaningful and summaries of the data for data warehouse.

Q. What is constraint based loading

Constraint based load order defines the order of loading the data into the multiple targets based on primary and foreign keys constraints.

Q. Why union transformation is active transformation?

the only condition for a transformation to bcum active is row number changes.

Now the thing is how a row number can change. Then there are

2 conditions:

1. either the no of rows coming in and going out is diff.

eg: in case of filter we have the data like

id name dept row_num

1 aa 4 1

2 bb 3 2

3 cc 4 3

and we have a filter condition like dept=4 then the o/p wld

b like

id name dept row_num

1 aa 4 1

3 cc 4 2

So row num changed and it is an active transformation

2. or the order of the row changes

eg: when Union transformation pulls in data, suppose we have

2 sources

sources1:

id name dept row_num

1 aa 4 1

2 bb 3 2

3 cc 4 3

source2:

id name dept row_num

4 aaa 4 4

5 bbb 3 5

6 ccc 4 6

it never restricts the data from any source so the data can

come in any manner

id name dept row_num old row_num

1 aa 4 1 1

4 aaa 4 2 4

5 bbb 3 3 5

2 bb 3 4 2

3 cc 4 5 3

6 ccc 4 6 6

so the row_num are changing . Thus we say that union is an active transformation

Q. What is use of batch file in informatica? How many types of batch file in informatica?

With the batch file, we can run sessions either in sequential or in concurrently.

Grouping of Sessions is known as Batch.

Two types of batches:

1)Sequential: Runs Sessions one after another.

2)Concurrent: Run the Sessions at the same time.

 If u have sessions with source-target dependencies u have to go for sequential batch to start the sessions one after another. If u have several independent sessions u can use concurrent batches Which run all the sessions at the same time

Q. What is joiner cache?

When we use the joiner transformation an integration service maintains the cache, all the records are stored in joiner cache. Joiner caches have 2 types of cache 1.Index cache 2. Joiner cache.

Index cache stores all the port values which are participated in the join condition and data cache have stored all ports which are not participated in the join condition. 

Q. What is the location of parameter file in Informatica?

$PMBWPARAM

Q. How can you display only hidden files in UNIX

$ ls -la

total 16

8 drwxrwxrwx 2 zzz yyy 4096 Apr 26 12:00 ./

8 drwxrwxrwx 9 zzz yyy 4096 Jul 31 16:59 ../

Correct answer is

ls -a|grep "^\."

$ls -a

Q. How to delete the data in the target table after loaded.

SQ---> Properties tab-->Post SQL 

delete from target_tablename

SQL statements executed using the source database connection, after a pipeline is run write post sql in target table as truncate table name. we have the property in session truncate option.

Q. What is polling in informatica?

It displays the updated information about the session in the monitor window. The monitor window displays the status of each session when you poll the Informatica server.

Q. How i will stop my workflow after 10 errors

Session level property error handling mention condition stop on errors: 10          

--->Config object –> Error Handling –> Stop on errors

Q. How can we calculate fact table size?

A fact table is multiple of combination of dimension tables 

ie if we want 2 find the fact table size of 3years of historical date with 200 products and 200 stores

3*365*200*200=fact table size

Q. Without using emailtask how will send a mail from informatica?

by using 'mailx' command in unix of shell scripting

Q. How will compare two mappings in two different repositories?

in the designer client , goto mapping tab there is one 

option that is 'compare', here we will compare two mappings in two different repository

in informatica designer go to mapping tab--->compare..

we can compare 2 folders within the same repository ..

we can compare 2 folders within different repository ..

Q. What is constraint based load order

Constraint based load order defines the order in which data loads into the multiple targets based on primary key and foreign key relationship.

Q. What is target load plan

Suppose i have 3 pipelines in a single mapping designer

emp  source--->sq--->tar1

dept source--->sq--->tar2

bonus source--->sq--->tar3

my requirement is to load first in tar2 then tar1 and then finally tar3

for this type of loading to control the extraction of data from source by source qualifier we use target load plan.

Q. What is meant by data driven.. in which scenario we use that..?

Data driven is available at session level. it says that when we r using update strategy t/r ,how the integration service fetches the data and how to update/insert row in the database log.

Data driven is nothing but instruct the source rows that should take action on target i.e(update,delete,reject,insert). If we use the update strategy transformation in a mapping then will select the data driven option in session.

Q. How to run workflow in unix?  

Syntax: pmcmd startworkflow -sv <service name> -d <domain name> -u <user name> -p <password> -f <folder name> <workflow name>

Example

Pmcmd start workflow –service

${INFA_SERVICE} -domain 

${INFA_DOMAIN} -uv xxx_PMCMD_ID -pv PSWD -folder  

${ETLFolder} -wait ${ETLWorkflow} \

Q. What is the main difference between a Joiner Transformation and Union Transformation?

Joiner Transformation merge horizontally

Union Transformation merge vertically

A joiner Transformation is used to join data from hertogenous database ie (Sql database and flat file) where has Union transformation is used to join data from 

the same relational sources.....(oracle table and another Oracle table)

Join Transformation combines data record horizontally based on join condition.

And combine data from two different sources having different metadata.

Join transformation supports heterogeneous, homogeneous data source.

Union Transformation combines data record vertically from multiple sources, having same metadata.

Union transformation also support heterogeneous data source.

Union transformation functions as UNION ALL set operator.

Q. What is constraint based loading exactly? And how to do this? I think it is when we have primary key-foreign key relationship. Is it correct?

Constraint Based Load order defines load the data into multiple targets depend on the primary key foreign key relation.

set the option is: Double click the session

Configure Object check the Constraint Based Loading

Q. Difference between top down(w.h inmon)and bottom up(ralph kimball)approach?

Top Down approach:-

As per W.H.INWON, first we need to build the Data warehouse after that we need to build up the DataMart   but this is so what difficult to maintain the DWH.

Bottom up approach;-

As per Ralph Kimbal, first we need to build up the Data Marts then we need to build up the Datawarehouse..

this approach is most useful in real time while creating the Data warehouse.

Q. What are the different caches used in informatica?

·         Static cache

·         Dynamic cache

·         Shared cache

·         Persistent cache

Q. What is the command to get the list of files in a directory in unix?

$ls -lrt

Q. How to import multiple flat files in to single target where there is no common column in the flat files

in workflow session properties in Mapping tab in properties choose Source filetype - Indirect

Give the Source filename : <file_path>

This <file_path> file should contain all the multiple files which you want to Load

Q. How to connect two or more table with single source qualifier?

Create a Oracle source with how much ever column you want and write the join query in SQL query override. But the column order and data type should be same as in the SQL query.

Q. How to call unconnected lookup in expression transformation?

:LKP.LKP_NAME(PORTS)

Q. What is diff between connected and unconnected lookup?

Connected lookup:

It is used to join the two tables

it returns multiple rows

it must be in mapping pipeline

u can implement lookup condition 

using connect lookup u can generate sequence numbers by

enabling dynamic lookup cache.

Unconnected lookup:

it returns single output through return port

it acts as a lookup function(:lkp)

it is called by another t/r.

not connected either source r target.

------

CONNECTED LOOKUP:

 >> It will participated in data pipeline

 >> It contains multiple inputs and multiple outputs.

  >> It supported static and dynamic cache.

UNCONNECTED LOOKUP:

 >> It will not participated in data pipeline

 >> It contains multiple inputs and single output.

  >> It supported static cache only.

Q. Types of partitioning in Informatica?

Partition 5 types

1.   Simple pass through

2.   Key range

3.   Hash

4.   Round robin

5.   Database

Q. Which transformation uses cache?

1.   Lookup transformation 

2.   Aggregator transformation

3.   Rank transformation

4.   Sorter transformation

5.   Joiner transformation

Q. Explain about union transformation?

A union transformation is a multiple input group transformation, which is used to merge the data from multiple sources similar to UNION All SQL statements to combine the results from 2 or more sql statements. 

  Similar to UNION All statement, the union transformation doesn't remove duplicate rows. It is an active transformation.

Q. Explain about Joiner transformation?

Joiner transformation is used to join source data from two related heterogeneous sources. However this can also be used to join data from the same source. Joiner t/r join sources with at least one matching column. It uses a condition that matches one or more pair of columns between the 2 sources.

    To configure a Joiner t/r various settings that we do are as below:

1) Master and detail source

2) Types of join

3) Condition of the join

Q. Explain about Lookup transformation?

Lookup t/r is used in a mapping to look up data in a relational table, flat file, view or synonym.

The informatica server queries the look up source based on the look up ports in the transformation. It compares look up t/r port values to look up source column values based on the look up condition.

Look up t/r is used to perform the below mentioned tasks:

1) To get a related value.

2) To perform a calculation.

3) To update SCD tables.

Q. How to identify this row for insert and this row for update in dynamic lookup cache?

Based on NEW LOOKUP ROW.. Informatica server indicates which one is insert and which one is update.

Newlookuprow- 0...no change

Newlookuprow- 1...Insert

Newlookuprow- 2...update

Q. How many ways can we implement SCD2?

1) Date range 

2) Flag 

3) Versioning

Q. How will you check the bottle necks in informatica? From where do you start checking?

You start as per this order

1.   Target

2.   Source

3.   Mapping

4.   Session

5.   System

Q. What is incremental aggregation?

When the aggregator transformation executes all the output data will get stored in the temporary location called aggregator cache. When the next time the mapping runs the aggregator transformation runs for the new records loaded after the first run. These output values will get incremented with the values in the aggregator cache. This is called incremental aggregation. By this way we can improve performance...

---------------------------

Incremental aggregation means applying only the captured changes in the source to aggregate calculations in a session.

   When the source changes only incrementally and if we can capture those changes, then we can configure the session to process only those changes. This allows informatica server to update target table incrementally, rather than forcing it to process the entire source and recalculate the same calculations each time you run the session. By doing this obviously the session performance increases.

Q. How can i explain my project architecture in interview..? Tell me your project flow from source to target..?

Project architecture is like

1. Source Systems: Like Mainframe,Oracle,People soft,DB2.

2. Landing tables: These are tables act like source. Used for easy to access, for backup purpose, as reusable for other mappings.

3. Staging tables: From landing tables we extract the data into staging tables after all validations done on the data.

4. Dimension/Facts: These are the tables those are used for analysis and make decisions by analyzing the data.

5. Aggregation tables: These tables have summarized data useful for managers who wants to view monthly wise sales, year wise sales etc. 

6. Reporting layer: 4 and 5 phases are useful for reporting developers to generate reports. I hope this answer helps you. 

Q. What type of transformation is not supported by mapplets?

·         Normalizer transformation

·         COBOL sources, joiner

·         XML source qualifier transformation

·         XML sources

·         Target definitions

·         Pre & Post Session stored procedures

·         Other mapplets

Q. How informatica recognizes mapping?

All are organized by Integration service.

Power center talks to Integration Service and Integration service talk to session. Session has mapping Structure. These are flow of Execution. 

Q. Can every transformation reusable? How?

Except source qualifier transformation, all transformations support reusable property. Reusable transformation developed in two ways.

1. In mapping which transformation do you want to reuse, select the transformation and double click on it, there you got option like make it as reusable transformation 

option. There you need to check the option for converting non reusable to reusable transformation. but except for source qualifier trans.

2. By using transformation developer

Q. What is Pre Sql and Post Sql?

Pre SQL means that the integration service runs SQL commands against the source database before it reads the data from source.

Post SQL means integration service runs SQL commands against target database after it writes to the target.

Q. Insert else update option in which situation we will use?

if the source table contain multiple records .if the  record specified in the associated port to insert into lookup cache. it does not find a record in the lookup cache when it is used find the particular record & change the data in the associated port.

----------------------

We set this property when the lookup TRFM uses dynamic cache and the session property TREAT SOURCE ROWS AS "Insert" has been set.

--------------------

This option we use when we want to maintain the history.

If records are not available in target table then it inserts the records in to target and records are available in target table then it updates the records.

Q. What is an incremental loading? in which situations we will use incremental loading?

Incremental Loading is an approach. Let suppose you a mapping for load the data from employee table to a employee_target table on the hire date basis. Again let suppose you already move the employee data from source to target up to the employees hire date 31-12-2009.Your organization now want to load data on employee_target today. Your target already have the data of that employees having hire date up to 31-12-2009.so you now pickup the source data which are hiring from 1-1-2010 to till date. That's why you needn't take the data before than that date, if you do that wrongly it is overhead for loading data again in target which is already exists. So in source qualifier you filter the records as per hire date and you can also parameterized the hire date that help from which date you want to load data upon target.

This is the concept of Incremental loading.

Q. What is target update override?

By Default the integration service updates the target based on key columns. But we might want to update non-key columns also, at that point of time we can override the 

UPDATE statement for each target in the mapping. The target override affects only when the source rows are marked as update by an update strategy in the mapping.

Q. What is the Mapping parameter and Mapping variable?

Mapping parameter: Mapping parameter is constant values that can be defined before mapping run. A mapping parameter reuses the mapping for various constant values. 

Mapping variable: Mapping variable is represent a value that can be change during the mapping run  that can be stored in repository the integration service retrieve that value from repository and incremental value for next run.

Q. What is rank and dense rank in informatica with any examples and give sql query for this both ranks

for eg: the file contains the records with column

100

200(repeated rows)

200

300

400

500

the rank function gives output as 

1

2

2

4

5

6

and dense rank gives

1

2

2

3

4

5

for eg: the file contains the records with column

empno sal

100  1000

200(repeated rows) 2000

200  3000

300 4000

400 5000 

500 6000 

Rank :

select rank() over (partition by empno order by sal) from emp

1

2

2

4

5

6

Dense Rank

select dense_rank() over (partition by empno order by sal) from emp

and dense rank gives

1

2

2

3

4

5

Q. What is the incremental aggregation?

The first time you run an upgraded session using incremental aggregation, the Integration Service upgrades the index and data cache files. If you want to partition a session using a mapping with incremental aggregation, the Integration Service realigns the index and data cache files.

Q. What is session parameter?

Parameter file is a text file where we can define the values to the parameters .session parameters are used for assign the database connection values

Q. What is mapping parameter?

A mapping parameter represents a constant value that can be defined before mapping run. A mapping parameter defines a parameter file which is saved with an extension.prm a mapping parameter reuse the various constant values.

Q. What is parameter file?

A parameter file can be a text file. Parameter file is to define the values for parameters and variables used in a session. A parameter file is a file created by text editor such as word pad or notepad. You can define the following values in parameter file

·         Mapping parameters

·         Mapping variables

·         Session parameters

Q. What is session override?

Session override is an option in informatica at session level. Here we can manually give a sql query which is issued to the database when the session runs. It is nothing but over riding the default sql which is generated by a particular transformation at mapping level.

Q. What are the diff. b/w informatica versions 8.1.1 and 8.6.1?

Little change in the Administrator Console. In 8.1.1 we can do all the creation of IS and repository Service, web service, Domain, node, grid ( if we have licensed version),In 8.6.1 the Informatica Admin console we can manage both Domain page and security page. Domain Page means all the above like creation of IS and repository Service, web service, Domain, node, grid ( if we have licensed version) etc. Security page means creation of users, privileges, LDAP configuration, Export Import user and Privileges etc.

Q. What are the uses of a Parameter file?

Parameter file is one which contains the values of mapping variables.

type this in notepad.save it .

foldername.sessionname

$$inputvalue1=

---------------------------------

Parameter files are created with an extension of .PRM

These are created to pass values those can be changed for Mapping Parameter and Session Parameter during mapping run. 

Mapping Parameters:

A Parameter is defined in a parameter file for which a Parameter is create already in the Mapping with Data Type , Precision and scale.

The Mapping parameter file syntax (xxxx.prm). 

[FolderName.WF:WorkFlowName.ST:SessionName]

$$ParameterName1=Value 

$$ParameterName2=Value

After that we have to select the properties Tab of Session and Set Parameter file name including physical path of this xxxx.prm file.

Session Parameters: 

The Session Parameter files syntax (yyyy.prm). 

[FolderName.SessionName]

$InputFileValue1=Path of the source Flat file

After that we have to select the properties Tab of Session and Set Parameter file name including physical path of this yyyy.prm file.

Do following changes in Mapping Tab of Source Qualifier's 

Properties section

  Attributes                values 

Source file Type ---------> Direct

Source File Directory  --------> Empty

Source File Name       --------> $InputFileValue1

Q. What is the default data driven operation in informatica?

This is default option for update strategy transformation.

The integration service follows instructions coded in update strategy within session mapping determine how to flag records for insert,delete,update,reject. If you do not data driven option setting, the integration service ignores update strategy transformations in the mapping.

Q. What is threshold error in informatica?

When the target is used by the update strategy DD_REJECT,DD_UPDATE and some limited count, then if it the number of rejected records exceed the count then the 

session ends with failed status.  This error is called Threshold Error.

Q. SO many times i saw "$PM parser error ". What is meant by PM?

PM: POWER MART

1) Parsing error will come for the input parameter to the lookup. 

2) Informatica is not able to resolve the input parameter CLASS for your lookup.

3) Check the Port CLASS exists as either input port or a variable port in your expression.

4) Check data type of CLASS and the data type of input parameter for your lookup.

Q. What is a candidate key?

A candidate key is a combination of attributes that can be uniquely used to identify a database record without any extraneous data (unique). Each table may have one or more candidate keys. One of these candidate keys is selected as the table primary key else are called Alternate Key.

Q. What is the difference between Bitmap and Btree index?

Bitmap index is used for repeating values.

ex: Gender: male/female

    Account status:Active/Inactive

Btree index is used for unique values. 

ex: empid.

Q. What is ThroughPut in Informatica?

Thoughtput is the rate at which power centre server read the rows in bytes from source or writes the rows in bytes into the target per second.

You can find this option in workflow monitor. Right click on session choose properties and Source/Target Statictics tab you can find thoughtput details for each instance of source and target.

Q. What are set operators in Oracle

UNION

UNION ALL

MINUS

INTERSECT

Q. How i can Schedule the Informatica job in "Unix Cron scheduling tool"?

Crontab

The crontab (cron derives from chronos, Greek for time; tab stands for table) command, found in Unix and Unix-like operating systems, is used to schedule commands to be executed periodically. To see what crontabs are currently running on your system, you can open a terminal and run:

sudo crontab -l

To edit the list of cronjobs you can run:

sudo crontab -e

This will open a the default editor (could be vi or pico, if you want you can change the default editor) to let us manipulate the crontab. If you save and exit the editor, all your cronjobs are saved into crontab. Cronjobs are written in the following format:

* * * * * /bin/execute/this/script.sh

Scheduling explained

As you can see there are 5 stars. The stars represent different date parts in the following order:

1.         minute (from 0 to 59) 

2.         hour (from 0 to 23) 

3.         day of month (from 1 to 31) 

4.         month (from 1 to 12) 

5.         day of week (from 0 to 6) (0=Sunday) 

Execute every minute

If you leave the star, or asterisk, it means every. Maybe 

that's a bit unclear. Let's use the the previous example 

again:

* * * * * /bin/execute/this/script.sh

They are all still asterisks! So this means 

execute /bin/execute/this/script.sh:

1.         every minute 

2.         of every hour 

3.         of every day of the month 

4.         of every month 

5.         and every day in the week. 

In short: This script is being executed every minute. 

Without exception.

Execute every Friday 1AM

So if we want to schedule the script to run at 1AM every 

Friday, we would need the following cronjob:

0 1 * * 5 /bin/execute/this/script.sh

Get it? The script is now being executed when the system 

clock hits:

1.         minute: 0 

2.         of hour: 1 

3.         of day of month: * (every day of month) 

4.         of month: * (every month) 

5.         and weekday: 5 (=Friday) 

Execute on weekdays 1AM

So if we want to schedule the script to run at 1AM every Friday, we would need the following cronjob:

0 1 * * 1-5 /bin/execute/this/script.sh

Get it? The script is now being executed when the system 

clock hits:

1.         minute: 0 

2.         of hour: 1 

3.         of day of month: * (every day of month) 

4.         of month: * (every month) 

5.         and weekday: 1-5 (=Monday til Friday) 

Execute 10 past after every hour on the 1st of every month

Here's another one, just for practicing

10 * 1 * * /bin/execute/this/script.sh

Fair enough, it takes some getting used to, but it offers great flexibility.

Q. Can anyone tell me the difference between persistence and dynamic caches? On which conditions we are using these caches?

Dynamic:--

1)When you use a dynamic cache, the Informatica Server updates the lookup cache as it passes rows to the target. 

2)In Dynamic, we can update catch will New data also.

3) Dynamic cache, Not Reusable

(when we need Updated cache data, That only we need Dynamic Cache)

Persistent:--

1)a Lookup transformation to use a non-persistent or persistent cache. The PowerCenter Server saves or deletes lookup cache files after a successful session based on the Lookup Cache Persistent property.

2) Persistent, we are not able to update the catch with New data.

3) Persistent catch is Reusable.

(When we need Previous Cache data, That only we need Persistent Cache)

----------------------------------

few more additions to the above answer.....

1. Dynamic lookup allows modifying cache where as Persistent lookup does not allow us to modify cache.

2. Dynamic lookup use 'newlookup row', a default port in the cache but persistent does use any default ports in cache.

3.As session completes dynamic cache removed but the persistent cache saved in informatica power centre server.

 

Q. How to obtain performance data for individual transformations?

There is a property at session level “Collect Performance Data“, you can select that property. It gives you performance details for all the transformations.

Q. List of Active and Passive Transformations in Informatica?

Active Transformation - An active transformation changes the number of rows that pass through the mapping.

• Source Qualifier Transformation
• Sorter Transformations
• Aggregator Transformations
• Filter Transformation
• Union Transformation
• Joiner Transformation
• Normalizer Transformation
• Rank Transformation
• Router Transformation
• Update Strategy Transformation
• Advanced External Procedure Transformation

Passive Transformation - Passive transformations do not change the number of rows that pass through the mapping.

• Expression Transformation
• Sequence Generator Transformation
• Lookup Transformation
• Stored Procedure Transformation
• XML Source Qualifier Transformation
• External Procedure Transformation

Q. Eliminating of duplicate records without using dynamic lookups?

Hi U can eliminate duplicate records by an simple one line SQL Query.

Select id, count (*) from seq1 group by id having count (*)>1;

Below are the ways to eliminate the duplicate records:

1. By enabling the option in Source Qualifier transformation as select distinct. 

2. By enabling the option in sorter transformation as select distinct. 

3.  By enabling all the values as group by in Aggregator transformation.

Q. Can anyone give idea on how do we perform test load in informatica? What do we test as part of test load in informatica?

With a test load, the Informatica Server reads and transforms data without writing to targets. The Informatica Server does everything, as if running the full session. The Informatica Server writes data to relational targets, but rolls back the data when the session completes. So, you can enable collect performance details property and analyze the how efficient your mapping is. If the session is running for a long time, you may like to find out the bottlenecks that are existing. It may be bottleneck of type target, source, mapping etc.

The basic idea behind test load is to see the behavior of Informatica Server with your session.

Q. What is ODS (Operational Data Store)?

A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into enterprise data architecture.

 An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse.

The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

Q. How many tasks are there in informatica?

·         Session Task

·         Email Task

·         Command Task

·         Assignment Task

·         Control Task

·         Decision Task

·         Event-Raise

·         Event- Wait

·         Timer Task

·         Link Task

Q. What are business components in Informatica?

·         Domains 

·         Nodes 

·         Services

 

Q. What is versioning?

It’s used to keep history of changes done on the mappings and workflows

1.   Check in: You check in when you are done with your changes so that everyone can see those changes.

2.   Check out: You check out from the main stream when you want to make any change to the mapping/workflow.

3.   Version history: It will show you all the changes made and who made it.

 

Q. Diff between $$$sessstarttime and sessstarttime?

$$$SessStartTime - Returns session start time as a string value (String datatype)
SESSSTARTTIME - Returns the date along with date timestamp (Date datatype)

Q. Difference between $,$$,$$$ in Informatica?

1. $ Refers
These are the system variables/Session Parameters like $Bad file,$input
file, $output file, $DB connection,$source,$target etc..
2. $$ Refers
User defined variables/Mapping Parameters like $$State,$$Time, $$Entity, $$Business_Date, $$SRC,etc. 

3. $$$ Refers
System Parameters like $$$SessStartTime

$$$SessStartTime returns the session start time as a string value. The format of the string depends on the database you are using.

$$$SessStartTime returns the session start time as a string value --> The format of the string depends on the database you are using.

Q. Finding Duplicate Rows based on Multiple Columns?

SELECT firstname, COUNT(firstname), surname, COUNT(surname), email, COUNT(email) FROM employee

GROUP BY firstname, surname, email

HAVING (COUNT(firstname) > 1) AND (COUNT(surname) > 1) AND (COUNT(email) > 1);

 

Q. Finding Nth Highest Salary in Oracle?

Pick out the Nth highest salary, say the 4th highest salary.

Select * from

(select ename,sal,dense_rank() over (order by sal desc) emp_rank from emp)

where emp_rank=4;

Q. Find out the third highest salary?

SELECT MIN(sal) FROM emp WHERE 

sal IN (SELECT  distinct TOP 3  sal FROM emp ORDER BY sal DESC);

Q. How do you handle error logic in Informatica? What are the transformations that you used while handling errors? How did you reload those error records in target? 

Row indicator: It generally happens when working with update strategy transformation. The writer/target rejects the rows going to the target

Column indicator:

D -Valid

o - Overflow

n - Null

t - Truncate

When the data is with nulls, or overflow it will be rejected to write the data to the target

The reject data is stored on reject files. You can check the data and reload the data in to the target using reject reload utility.

Q. Difference between STOP and ABORT?

Stop - If the Integration Service is executing a Session task when you issue the stop command, the Integration Service stops reading data. It continues processing and writing data and committing data to targets. If the Integration Service cannot finish processing and committing data, you can issue the abort command.

Abort - The Integration Service handles the abort command for the Session task like the stop command, except it has a timeout period of 60 seconds. If the Integration Service cannot finish processing and committing data within the timeout period, it kills the DTM process and terminates the session.

Q. What is inline view?

An inline view is term given to sub query in FROM clause of query which can be used as table. Inline view effectively is a named sub query

Ex : Select Tab1.col1,Tab1.col.2,Inview.col1,Inview.Col2
From Tab1, (Select statement) Inview
Where Tab1.col1=Inview.col1

SELECT DNAME, ENAME, SAL FROM EMP ,
(SELECT DNAME, DEPTNO FROM DEPT) D
WHERE A.DEPTNO = B.DEPTNO

In the above query (SELECT DNAME, DEPTNO FROM DEPT) D is the inline view.

Inline views are determined at runtime, and in contrast to normal view they are not stored in the data dictionary,

Disadvantage of using this is

1. Separate view need to be created which is an overhead
2. Extra time taken in parsing of view

This problem is solved by inline view by using select statement in sub query and using that as table.

Advantage of using inline views:

1. Better query performance
2. Better visibility of code

Practical use of Inline views:

1. Joining Grouped data with non grouped data
2. Getting data to use in another query

Q. What is generated key and generated column id in normalizer transformation?

·         The integration service increments the generated key (GK) sequence number each time it process a source row. When the source row contains a multiple-occurring column or a multiple-occurring group of columns, the normalizer transformation returns a row for each occurrence. Each row contains the same generated key value.

·         The normalizer transformation has a generated column ID (GCID) port for each multiple-occurring column. The GCID is an index for the instance of the multiple-occurring data. For example, if a column occurs 3 times in a source record, the normalizer returns a value of 1, 2 or 3 in the generated column ID.

Q. What is difference between SUBSTR and INSTR?

INSTR function search string for sub-string and returns an integer indicating the position of the character in string that is the first character of this occurrence.

SUBSTR function returns a portion of string, beginning at character position, substring_length characters long. SUBSTR calculates lengths using characters as defined by the input character set.

Q. What are different Oracle database objects?

·         TABLES

·         VIEWS

·         INDEXES

·         SYNONYMS

·         SEQUENCES

·         TABLESPACES

Q. What is @@ERROR?

The @@ERROR automatic variable returns the error code of the last Transact-SQL statement. If there was no error, @@ERROR returns zero. Because @@ERROR is reset after each Transact-SQL statement, it must be saved to a variable if it is needed to process it further after checking it.

Q. What is difference between co-related sub query and nested sub query?

Correlated subquery runs once for each row selected by the outer query. It contains a reference to a value from the row selected by the outer query.

Nested subquery runs only once for the entire nesting (outer) query. It does not contain any reference to the outer query row.

For example,

Correlated Subquery:

Select e1.empname, e1.basicsal, e1.deptno from emp e1 where e1.basicsal = (select max(basicsal) from emp e2 where e2.deptno = e1.deptno)

Nested Subquery:

Select empname, basicsal, deptno from emp where (deptno, basicsal) in (select deptno, max(basicsal) from emp group by deptno)

 

Q. How does one escape special characters when building SQL queries?

The LIKE keyword allows for string searches. The ‘_’ wild card character is used to match exactly one character,  ‘%’ is used to match zero or more occurrences of any characters. These characters can be escaped in SQL. Example:

SELECT name FROM emp WHERE id LIKE ‘%\_%’ ESCAPE ‘\’;

Use two quotes for every one displayed. Example:

SELECT ‘Franks”s Oracle site’ FROM DUAL;
SELECT ‘A ”quoted” word.’ FROM DUAL;
SELECT ‘A ””double quoted”” word.’ FROM DUAL;

 

Q. Difference between Surrogate key and Primary key?

Surrogate key:

1.   Query processing is fast.

2.   It is only numeric

3.   Developer develops the surrogate key using sequence generator transformation.

4.   Eg: 12453

Primary key:

1.   Query processing is slow

2.   Can be alpha numeric

3.   Source system gives the primary key.

4.   Eg: C10999

Q. How does one eliminate duplicate rows in an Oracle Table?

Method 1:

DELETE from table_name A
where rowid > (select min(rowid) from table_name B where A.key_values = B.key_values);

Method 2:

Create table table_name2 as select distinct * from table_name1;
drop table table_name1;
rename table table_name2 as table_name1;

In this method, all the indexes,constraints,triggers etc have to be re-created.

Method 3:

DELETE from table_name t1
where exists (select ‘x’ from table_name t2 where t1.key_value=t2.key_value and t1.rowid > t2.rowid)

Method 4:

DELETE from table_name where rowid not in (select max(rowid) from my_table group by key_value )

Q. Query to retrieve Nth row from an Oracle table?

The query is as follows:

select * from my_table where rownum <= n
MINUS
select * from my_table where rownum < n;

Q. How does the server recognize the source and target databases? 

If it is relational - By using ODBC connection

FTP connection - By using flat file

Q. What are the different types of indexes supported by Oracle?

1.   B-tree index

2.   B-tree cluster index

3.   Hash cluster index

4.   Reverse key index

5.   Bitmap index

6.   Function Based index

 

Q. Types of Normalizer transformation?

There are two types of Normalizer transformation.

VSAM Normalizer transformation

A non-reusable transformation that is a Source Qualifier transformation for a COBOL source. The Mapping Designer creates VSAM Normalizer columns from a COBOL source in a mapping. The column attributes are read-only. The VSAM Normalizer receives a multiple-occurring source column through one input port.

Pipeline Normalizer transformation

A transformation that processes multiple-occurring data from relational tables or flat files. You might choose this option when you want to process multiple-occurring data from another transformation in the mapping.

A VSAM Normalizer transformation has one input port for a multiple-occurring column. A pipeline Normalizer transformation has multiple input ports for a multiple-occurring column.
When you create a Normalizer transformation in the Transformation Developer, you create a pipeline Normalizer transformation by default. When you create a pipeline Normalizer transformation, you define the columns based on the data the transformation receives from another type of transformation such as a Source Qualifier transformation.

The Normalizer transformation has one output port for each single-occurring input port.

Q. What are all the transformation you used if source as XML file?

·         XML Source Qualifier

·         XML Parser

·         XML Generator

 

Q. List the files in ascending order in UNIX?

ls  -lt (sort by last date modified) 

ls –ltr (reverse)

ls –lS (sort by size of the file)

Q. How do identify the empty line in a flat file in UNIX? How to remove it?

grep –v “^$” filename

Q. How do send the session report (.txt) to manager after session is completed?

Email variable – %a (attach the file) %g – attach session log file

Q. How to check all the running processes in UNIX?

$> ps –ef

Q. How can i display only and only hidden file in the current directory?

ls -a|grep "^\."

Q. How to display the first 10 lines of a file?

# head -10 logfile

Q. How to display the last 10 lines of a file?

# tail -10 logfile

Q. How did you schedule sessions in your project?

1.   Run once – Set 2 parameter date and time when session should start.

2.   Run Every – Informatica server run session at regular interval as we configured, parameter Days, hour, minutes, end on, end after, forever.

3.   Customized repeat – Repeat every 2 days, daily frequency hr, min, every week, every month.

Q. What is lookup override?

This feature is similar to entering a custom query in a Source Qualifier transformation. When entering a Lookup SQL Override, you can enter the entire override, or generate and edit the default SQL statement.

The lookup query override can include WHERE clause.

Q. What is Sql Override?

The Source Qualifier provides the SQL Query option to override the default query. You can enter any SQL statement supported by your source database. You might enter your own SELECT statement, or have the database perform aggregate calculations, or call a stored procedure or stored function to read the data and perform some tasks.

Q. How to get sequence value using Expression?

v_temp = v_temp+1

o_seq = IIF(ISNULL(v_temp), 0, v_temp)

Q. How to get Unique Record?

Source –> SQ –> SRT –> EXP –> FLT OR RTR –> TGT

In Expression:

flag = Decode(true,eid=pre_eid, ‘Y’,'N’)

flag_out = flag

pre_eid = eid

Q. What are the different transaction levels available in transaction control transformation (TCL)?

The following are the transaction levels or built-in variables:

·         TC_CONTINUE_TRANSACTION: The Integration Service does not perform any transaction change for this row. This is the default value of the expression.

·         TC_COMMIT_BEFORE: The Integration Service commits the transaction, begins a new transaction, and writes the current row to the target. The current row is in the new transaction.

·         TC_COMMIT_AFTER: The Integration Service writes the current row to the target, commits the transaction, and begins a new transaction. The current row is in the committed transaction.

·         TC_ROLLBACK_BEFORE: The Integration Service rolls back the current transaction, begins a new transaction, and writes the current row to the target. The current row is in the new transaction.

·         TC_ROLLBACK_AFTER: The Integration Service writes the current row to the target, rolls back the transaction, and begins a new transaction. The current row is in the rolled back transaction.

Q. What is difference between grep and find?

Grep is used for finding any string in the file. 

Syntax - grep <String> <filename> 

Example - grep 'compu' details.txt

Display the whole line,in which line compu string is found. 

Find is used to find the file or directory in given path, 

Syntax - find <filename> 

Example - find compu*

Display all file names starting with computer

Q. What are the difference between DDL, DML and DCL commands?

DDL is Data Definition Language statements

·         CREATE – to create objects in the database

·         ALTER – alters the structure of the database

·         DROP – delete objects from the database

·         TRUNCATE – remove all records from a table, including all spaces allocated for the records are removed

·         COMMENT – add comments to the data dictionary

·         GRANT – gives user’s access privileges to database

·         REVOKE – withdraw access privileges given with the GRANT command

DML is Data Manipulation Language statements

·         SELECT – retrieve data from the a database

·         INSERT – insert data into a table

·         UPDATE – updates existing data within a table

·         DELETE – deletes all records from a table, the space for the records remain

·         CALL – call a PL/SQL or Java subprogram

·         EXPLAIN PLAN – explain access path to data

·         LOCK TABLE – control concurrency

DCL is Data Control Language statements

·         COMMIT – save work done

·         SAVEPOINT – identify a point in a transaction to which you can later roll back

·         ROLLBACK – restore database to original since the last COMMIT

·         SET TRANSACTION – Change transaction options like what rollback segment to use

Q. What is Stored Procedure?

A stored procedure is a named group of SQL statements that have been previously created and stored in the server database. Stored procedures accept input parameters so that a single procedure can be used over the network by several clients using different input data. And when the procedure is modified, all clients automatically get the new version. Stored procedures reduce network traffic and improve performance. Stored procedures can be used to help ensure the integrity of the database.

Q. What is View?

A view is a tailored presentation of the data contained in one or more tables (or other views). Unlike a table, a view is not allocated any storage space, nor does a view actually contain data; rather, a view is defined by a query that extracts or derives data from the tables the view references. These tables are called base tables.

Views present a different representation of the data that resides within the base tables. Views are very powerful because they allow you to tailor the presentation of data to different types of users.

Views are often used to:

·         Provide an additional level of table security by restricting access to a predetermined set of rows and/or columns of a table

·         Hide data complexity

·         Simplify commands for the user

·         Present the data in a different perspective from that of the base table

·         Isolate applications from changes in definitions of base tables

·         Express a query that cannot be expressed without using a view

Q. What is Trigger?

A trigger is a SQL procedure that initiates an action when an event (INSERT, DELETE or UPDATE) occurs. Triggers are stored in and managed by the DBMS. Triggers are used to maintain the referential integrity of data by changing the data in a systematic fashion. A trigger cannot be called or executed; the DBMS automatically fires the trigger as a result of a data modification to the associated table. Triggers can be viewed as similar to stored procedures in that both consist of procedural logic that is stored at the database level. Stored procedures, however, are not event-drive and are not attached to a specific table as triggers are. Stored procedures are explicitly executed by invoking a CALL to the procedure while triggers are implicitly executed. In addition, triggers can also execute stored Procedures.

Nested Trigger: A trigger can also contain INSERT, UPDATE and DELETE logic within itself, so when the trigger is fired because of data modification it can also cause another data modification, thereby firing another trigger. A trigger that contains data modification logic within itself is called a nested trigger.

Q. What is View?

A simple view can be thought of as a subset of a table. It can be used for retrieving data, as well as updating or deleting rows. Rows updated or deleted in the view are updated or deleted in the table the view was created with. It should also be noted that as data in the original table changes, so does data in the view, as views are the way to look at part of the original table. The results of using a view are not permanently stored in the database. The data accessed through a view is actually constructed using standard T-SQL select command and can come from one to many different base tables or even other views.

Q. What is Index?

An index is a physical structure containing pointers to the data. Indices are created in an existing table to locate rows more quickly and efficiently. It is possible to create an index on one or more columns of a table, and each index is given a name. The users cannot see the indexes; they are just used to speed up queries. Effective indexes are one of the best ways to improve performance in a database application. A table scan happens when there is no index available to help a query. In a table scan SQL Server examines every row in the table to satisfy the query results. Table scans are sometimes unavoidable, but on large tables, scans have a terrific impact on performance. Clustered indexes define the physical sorting of a database table’s rows in the storage media. For this reason, each database table may

have only one clustered index. Non-clustered indexes are created outside of the database table and contain a sorted list of references to the table itself.

Q. What is the difference between clustered and a non-clustered index?

A clustered index is a special type of index that reorders the way records in the table are physically stored. Therefore table can have only one clustered index. The leaf nodes of a clustered index contain the data pages. A nonclustered index is a special type of index in which the logical order of the index does not match the physical stored order of the rows on disk. The leaf node of a nonclustered index does not consist of the data pages. Instead, the leaf nodes contain index rows.

Q. What is Cursor?

Cursor is a database object used by applications to manipulate data in a set on a row-by row basis, instead of the typical SQL commands that operate on all the rows in the set at one time.

In order to work with a cursor we need to perform some steps in the following order:

·         Declare cursor

·         Open cursor

·         Fetch row from the cursor

·         Process fetched row

·         Close cursor

·         Deallocate cursor

Q. What is the difference between a HAVING CLAUSE and a WHERE CLAUSE?

1. Specifies a search condition for a group or an aggregate. HAVING can be used only with the SELECT statement.

2. HAVING is typically used in a GROUP BY clause. When GROUP BY is not used, HAVING behaves like a WHERE clause.

3. Having Clause is basically used only with the GROUP BY function in a query. WHERE Clause is applied to each row before they are part of the GROUP BY function in a query.

RANK CACHE

Sample Rank Mapping

When the Power Center Server runs a session with a Rank transformation, it compares an input row with rows in the data cache. If the input row out-ranks a Stored row, the Power Center Server replaces the stored row with the input row.

Example: Power Center caches the first 5 rows if we are finding top 5 salaried Employees. When 6th row is read, it compares it with 5 rows in cache and places it in Cache is needed.

1) RANK INDEX CACHE:

The index cache holds group information from the group by ports. If we are Using Group By on DEPTNO, then this cache stores values 10, 20, 30 etc.

All Group By Columns are in RANK INDEX CACHE. Ex. DEPTNO

2) RANK DATA CACHE:

It holds row data until the Power Center Server completes the ranking and is generally larger than the index cache. To reduce the data cache size, connect only the necessary input/output ports to subsequent transformations.

All Variable ports if there, Rank Port, All ports going out from RANK Transformations are stored in RANK DATA CACHE.

Example: All ports except DEPTNO In our mapping example.

Aggregator Caches

1.   The Power Center Server stores data in the aggregate cache until it completes Aggregate calculations.

2.   It stores group values in an index cache and row data in the data cache. If the Power Center Server requires more space, it stores overflow values in cache files.

Note: The Power Center Server uses memory to process an Aggregator transformation with sorted ports. It does not use cache memory. We do not need to configure cache memory for Aggregator transformations that use sorted ports.

1) Aggregator Index Cache:

The index cache holds group information from the group by ports. If we are using Group By on DEPTNO, then this cache stores values 10, 20, 30 etc.

·         All Group By Columns are in AGGREGATOR INDEX CACHE. Ex. DEPTNO

2) Aggregator Data Cache:

DATA CACHE is generally larger than the AGGREGATOR INDEX CACHE.

Columns in Data Cache:

·         Variable ports if any

·         Non group by input/output ports.

·         Non group by input ports used in non-aggregate output expression.

·         Port containing aggregate function

JOINER CACHES

Joiner always caches the MASTER table. We cannot disable caching. It builds Index cache and Data Cache based on MASTER table.

1) Joiner Index Cache:

All Columns of MASTER table used in Join condition are in JOINER INDEX CACHE.

 Example: DEPTNO in our mapping.

2) Joiner Data Cache:

Master column not in join condition and used for output to other transformation or target table are in Data Cache.

 Example: DNAME and LOC in our mapping example.

Lookup Cache Files

1. Lookup Index Cache:

Stores data for the columns used in the lookup condition.

2. Lookup Data Cache:

·         For a connected Lookup transformation, stores data for the connected output ports, not including ports used in the lookup condition.

·         For an unconnected Lookup transformation, stores data from the return port.

OLTP and OLAP

Logical Data Modeling Vs Physical Data Modeling

Router Transformation And Filter Transformation

Source Qualifier And Lookup Transformation

Mapping And Mapplet

Joiner Transformation And Lookup Transformation

Dimension Table and Fact Table

Connected Lookup and Unconnected Lookup

Connected Lookup	Unconnected Lookup
Receives input values directly from the pipeline.	Receives input values from the result of a :LKP expression in another transformation.
We can use a dynamic or static cache.	We can use a static cache.
Cache includes all lookup columns used in the mapping.	Cache includes all lookup/output ports in the lookup condition and the lookup/return port.
If there is no match for the lookup condition, the Power Center Server returns the default value for all output ports.	If there is no match for the lookup condition, the Power Center Server returns NULL.
If there is a match for the lookup condition, the Power Center Server returns the result of the lookup condition for all lookup/output ports.	If there is a match for the lookup condition, the Power Center Server returns the result of the lookup condition into the return port.
Pass multiple output values to another transformation.	Pass one output value to another transformation.
Supports user-defined default values	Does not support user-defined default values.

Cache Comparison

Persistence and Dynamic Caches

Dynamic
1) When you use a dynamic cache, the Informatica Server updates the lookup cache as it passes rows to the target.

2) In Dynamic, we can update catch will new data also.

3) Dynamic cache, Not Reusable.

(When we need updated cache data, That only we need Dynamic Cache)

Persistent
1) A Lookup transformation to use a non-persistent or persistent cache. The PowerCenter Server saves or deletes lookup cache files after a successful session based on the Lookup Cache Persistent property.

2) Persistent, we are not able to update the catch with new data.

3) Persistent catch is Reusable.

(When we need previous cache data, that only we need Persistent Cache)

View And Materialized View

Star Schema And Snow Flake Schema

Informatica - Transformations

In Informatica, Transformations help to transform the source data according to the requirements of target system and it ensures the quality of the data being loaded into target.
Transformations are of two types: Active and Passive.

Active Transformation
An active transformation can change the number of rows that pass through it from source to target. (i.e) It eliminates rows that do not meet the condition in transformation.

Passive Transformation
A passive transformation does not change the number of rows that pass through it (i.e) It passes all rows through the transformation.

Transformations can be Connected or Unconnected.

Connected Transformation
Connected transformation is connected to other transformations or directly to target table in the mapping.

Unconnected Transformation
An unconnected transformation is not connected to other transformations in the mapping. It is called within another transformation, and returns a value to that transformation.

Following are the list of Transformations available in Informatica:
Aggregator Transformation
Expression Transformation
Filter Transformation
Joiner Transformation
Lookup Transformation
Normalizer Transformation
Rank Transformation
Router Transformation
Sequence Generator Transformation
Stored Procedure Transformation
Sorter Transformation
Update Strategy Transformation
XML Source Qualifier Transformation

In the following pages, we will explain all the above Informatica Transformations and their significances in the ETL process in detail.

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Aggregator Transformation

Aggregator transformation is an Active and Connected transformation.

This transformation is useful to perform calculations such as averages and sums (mainly to perform calculations on multiple rows or groups).

For example, to calculate total of daily sales or to calculate average of monthly or yearly sales. Aggregate functions such as AVG, FIRST, COUNT, PERCENTILE, MAX, SUM etc. can be used in aggregate transformation.

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Expression Transformation

Expression transformation is a Passive and Connected transformation.

This can be used to calculate values in a single row before writing to the target.

For example, to calculate discount of each product

or to concatenate first and last names

or to convert date to a string field.

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Filter Transformation

Filter transformation is an Active and Connected transformation.

This can be used to filter rows in a mapping that do not meet the condition.

For example,

To know all the employees who are working in Department 10 or

To find out the products that falls between the rate category $500 and $1000.

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Joiner Transformation

Joiner Transformation is an Active and Connected transformation. This can be used to join two sources coming from two different locations or from same location. For example, to join a flat file and a relational source or to join two flat files or to join a relational source and a XML source.

In order to join two sources, there must be at least one matching port. While joining two sources it is a must to specify one source as master and the other as detail.

The Joiner transformation supports the following types of joins:

1)Normal

2)Master Outer

3)Detail Outer

4)Full Outer

Normal join discards all the rows of data from the master and detail source that do not match, based on the condition.

Master outer join discards all the unmatched rows from the master source and keeps all the rows from the detail source and the matching rows from the master source.

Detail outer join keeps all rows of data from the master source and the matching rows from the detail source. It discards the unmatched rows from the detail source.

Full outer join keeps all rows of data from both the master and detail sources.

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Lookup transformation 

Lookup transformation is Passive and it can be both Connected and UnConnected as well. It is used to look up data in a relational table, view, or synonym. Lookup definition can be imported either from source or from target tables.

For example, if we want to retrieve all the sales of a product with an ID 10 and assume that the sales data resides in another table. Here instead of using the sales table as one more source, use Lookup transformation to lookup the data for the product, with ID 10 in sales table.

Connected lookup receives input values directly from mapping pipeline whereas

Unconnected lookup receives values from: LKP expression from another transformation.

Connected lookup returns multiple columns from the same row whereas

Unconnected lookup has one return port and returns one column from each row.


Connected lookup supports user-defined default values whereas

Unconnected lookup does not support user defined values.

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Normalizer Transformation

Normalizer Transformation is an Active and Connected transformation.

It is used mainly with COBOL sources where most of the time data is stored in de-normalized format.

Also, Normalizer transformation can be used to create multiple rows from a single row of data.

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Rank Transformation

Rank transformation is an Active and Connected transformation.

It is used to select the top or bottom rank of data.

For example,

To select top 10 Regions where the sales volume was very high

or

To select 10 lowest priced products.

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Router Transformation

Router is an Active and Connected transformation. It is similar to filter transformation.

The only difference is, filter transformation drops the data that do not meet the condition whereas router has an option to capture the data that do not meet the condition. It is useful to test multiple conditions.

It has input, output and default groups.

For example, if we want to filter data like where State=Michigan, State=California, State=New York and all other States. It’s easy to route data to different tables.

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Sequence Generator Transformation

Sequence Generator transformation is a Passive and Connected transformation. It is used to create unique primary key values or cycle through a sequential range of numbers or to replace missing keys.

It has two output ports to connect transformations. By default it has two fields CURRVAL and NEXTVAL (You cannot add ports to this transformation).

NEXTVAL port generates a sequence of numbers by connecting it to a transformation or target. CURRVAL is the NEXTVAL value plus one or NEXTVAL plus the Increment By value.

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Sorter Transformation

Sorter transformation is a Connected and an Active transformation.

It allows sorting data either in ascending or descending order according to a specified field.

Also used to configure for case-sensitive sorting, and specify whether the output rows should be distinct.

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Source Qualifier Transformation

Source Qualifier transformation is an Active and Connected transformation. When adding a relational or a flat file source definition to a mapping, it is must to connect it to a Source Qualifier transformation.

The Source Qualifier performs the various tasks such as

Overriding Default SQL query,

Filtering records;

join data from two or more tables etc.

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Stored Procedure Transformation

Stored Procedure transformation is a Passive and Connected & Unconnected transformation. It is useful to automate time-consuming tasks and it is also used in error handling, to drop and recreate indexes and to determine the space in database, a specialized calculation etc.

The stored procedure must exist in the database before creating a Stored Procedure transformation, and the stored procedure can exist in a source, target, or any database with a valid connection to the Informatica Server. Stored Procedure is an executable script with SQL statements and control statements, user-defined variables and conditional statements.

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Update Strategy Transformation

Update strategy transformation is an Active and Connected transformation.

It is used to update data in target table, either to maintain history of data or recent changes.

You can specify how to treat source rows in table, insert, update, delete or data driven.

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XML Source Qualifier Transformation

XML Source Qualifier is a Passive and Connected transformation.

XML Source Qualifier is used only with an XML source definition.

It represents the data elements that the Informatica Server reads when it executes a session with XML sources.

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Constraint-Based Loading

In the Workflow Manager, you can specify constraint-based loading for a session. When you select this option, the Integration Service orders the target load on a row-by-row basis. For every row generated by an active source, the Integration Service loads the corresponding transformed row first to the primary key table, then to any foreign key tables. Constraint-based loading depends on the following requirements:

Active source: Related target tables must have the same active source.

Key relationships: Target tables must have key relationships.

Target connection groups: Targets must be in one target connection group.

Treat rows as insert. Use this option when you insert into the target. You cannot use updates with constraint based loading.

Active Source:

When target tables receive rows from different active sources, the Integration Service reverts to normal loading for those tables, but loads all other targets in the session using constraint-based loading when possible. For example, a mapping contains three distinct pipelines. The first two contain a source, source qualifier, and target. Since these two targets receive data from different active sources, the Integration Service reverts to normal loading for both targets. The third pipeline contains a source, Normalizer, and two targets. Since these two targets share a single active source (the Normalizer), the Integration Service performs constraint-based loading: loading the primary key table first, then the foreign key table.

Key Relationships:

When target tables have no key relationships, the Integration Service does not perform constraint-based loading.

Similarly, when target tables have circular key relationships, the Integration Service reverts to a normal load. For example, you have one target containing a primary key and a foreign key related to the primary key in a second target. The second target also contains a foreign key that references the primary key in the first target. The Integration Service cannot enforce constraint-based loading for these tables. It reverts to a normal load.

Target Connection Groups:

The Integration Service enforces constraint-based loading for targets in the same target connection group. If you want to specify constraint-based loading for multiple targets that receive data from the same active source, you must verify the tables are in the same target connection group. If the tables with the primary key-foreign key relationship are in different target connection groups, the Integration Service cannot enforce constraint-based loading when you run the workflow. To verify that all targets are in the same target connection group, complete the following tasks:

·         Verify all targets are in the same target load order group and receive data from the same active source.

·         Use the default partition properties and do not add partitions or partition points.

·         Define the same target type for all targets in the session properties.

·         Define the same database connection name for all targets in the session properties.

·         Choose normal mode for the target load type for all targets in the session properties.

Treat Rows as Insert:

Use constraint-based loading when the session option Treat Source Rows As is set to insert. You might get inconsistent data if you select a different Treat Source Rows As option and you configure the session for constraint-based loading.

When the mapping contains Update Strategy transformations and you need to load data to a primary key table first, split the mapping using one of the following options:

·         Load primary key table in one mapping and dependent tables in another mapping. Use constraint-based loading to load the primary table.

·         Perform inserts in one mapping and updates in another mapping.

Constraint-based loading does not affect the target load ordering of the mapping. Target load ordering defines the order the Integration Service reads the sources in each target load order group in the mapping. A target load order group is a collection of source qualifiers, transformations, and targets linked together in a mapping. Constraint based loading establishes the order in which the Integration Service loads individual targets within a set of targets receiving data from a single source qualifier.

Example

The following mapping is configured to perform constraint-based loading:

In the first pipeline, target T_1 has a primary key, T_2 and T_3 contain foreign keys referencing the T1 primary key. T_3 has a primary key that T_4 references as a foreign key.

Since these tables receive records from a single active source, SQ_A, the Integration Service loads rows to the target in the following order:

1. T_1

2. T_2 and T_3 (in no particular order)

3. T_4

The Integration Service loads T_1 first because it has no foreign key dependencies and contains a primary key referenced by T_2 and T_3. The Integration Service then loads T_2 and T_3, but since T_2 and T_3 have no dependencies, they are not loaded in any particular order. The Integration Service loads T_4 last, because it has a foreign key that references a primary key in T_3.After loading the first set of targets, the Integration Service begins reading source B. If there are no key relationships between T_5 and T_6, the Integration Service reverts to a normal load for both targets.

If T_6 has a foreign key that references a primary key in T_5, since T_5 and T_6 receive data from a single active source, the Aggregator AGGTRANS, the Integration Service loads rows to the tables in the following order:

T_5

T_6

T_1, T_2, T_3, and T_4 are in one target connection group if you use the same database connection for each target, and you use the default partition properties. T_5 and T_6 are in another target connection group together if you use the same database connection for each target and you use the default partition properties. The Integration Service includes T_5 and T_6 in a different target connection group because they are in a different target load order group from the first four targets.

Enabling Constraint-Based Loading:

When you enable constraint-based loading, the Integration Service orders the target load on a row-by-row basis. To enable constraint-based loading:

1.   In the General Options settings of the Properties tab, choose Insert for the Treat Source Rows As property.

2.   Click the Config Object tab. In the Advanced settings, select Constraint Based Load Ordering.

3.   Click OK.

Target Load Plan

When you use a mapplet in a mapping, the Mapping Designer lets you set the target load plan for sources within the mapplet.

Setting the Target Load Order

You can configure the target load order for a mapping containing any type of target definition. In the Designer, you can set the order in which the Integration Service sends rows to targets in different target load order groups in a mapping. A target load order group is the collection of source qualifiers, transformations, and targets linked together in a mapping. You can set the target load order if you want to maintain referential integrity when inserting, deleting, or updating tables that have the primary key and foreign key constraints.

The Integration Service reads sources in a target load order group concurrently, and it processes target load order groups sequentially.

To specify the order in which the Integration Service sends data to targets, create one source qualifier for each target within a mapping. To set the target load order, you then determine in which order the Integration Service reads each source in the mapping.

The following figure shows two target load order groups in one mapping:

In this mapping, the first target load order group includes ITEMS, SQ_ITEMS, and T_ITEMS. The second target load order group includes all other objects in the mapping, including the TOTAL_ORDERS target. The Integration Service processes the first target load order group, and then the second target load order group.

When it processes the second target load order group, it reads data from both sources at the same time.

To set the target load order:

·         Create a mapping that contains multiple target load order groups.

·         Click Mappings > Target Load Plan.

·         The Target Load Plan dialog box lists all Source Qualifier transformations in the mapping and the targets that receive data from each source qualifier.

·         Select a source qualifier from the list.

·         Click the Up and Down buttons to move the source qualifier within the load order.

·         Repeat steps 3 to 4 for other source qualifiers you want to reorder. Click OK.

Mapping Parameters & Variables

Mapping parameters and variables represent values in mappings and mapplets.

When we use a mapping parameter or variable in a mapping, first we declare the mapping parameter or variable for use in each mapplet or mapping. Then, we define a value for the mapping parameter or variable before we run the session.

Mapping Parameters

A mapping parameter represents a constant value that we can define before running a session.

A mapping parameter retains the same value throughout the entire session.

Example: When we want to extract records of a particular month during ETL process, we will create a Mapping Parameter of data type and use it in query to compare it with the timestamp field in SQL override.

After we create a parameter, it appears in the Expression Editor.

We can then use the parameter in any expression in the mapplet or mapping.

We can also use parameters in a source qualifier filter, user-defined join, or extract override, and in the Expression Editor of reusable transformations.

Mapping Variables

Unlike mapping parameters, mapping variables are values that can change between sessions.

·         The Integration Service saves the latest value of a mapping variable to the repository at the end of each successful session.

·         We can override a saved value with the parameter file.

·         We can also clear all saved values for the session in the Workflow Manager.

We might use a mapping variable to perform an incremental read of the source. For example, we have a source table containing time stamped transactions and we want to evaluate the transactions on a daily basis. Instead of manually entering a session override to filter source data each time we run the session, we can create a mapping variable, $$IncludeDateTime. In the source qualifier, create a filter to read only rows whose transaction date equals $$IncludeDateTime, such as:

TIMESTAMP = $$IncludeDateTime

In the mapping, use a variable function to set the variable value to increment one day each time the session runs. If we set the initial value of $$IncludeDateTime to 8/1/2004, the first time the Integration Service runs the session, it reads only rows dated 8/1/2004. During the session, the Integration Service sets $$IncludeDateTime to 8/2/2004. It saves 8/2/2004 to the repository at the end of the session. The next time it runs the session, it reads only rows from August 2, 2004.

Used in following transformations:

Expression

Filter

Router

Update Strategy

Initial and Default Value:

When we declare a mapping parameter or variable in a mapping or a mapplet, we can enter an initial value. When the Integration Service needs an initial value, and we did not declare an initial value for the parameter or variable, the Integration Service uses a default value based on the data type of the parameter or variable.

Data ->Default Value

Numeric ->0

String ->Empty String

Date time ->1/1/1

Variable Values: Start value and current value of a mapping variable

Start Value:

The start value is the value of the variable at the start of the session. The Integration Service looks for the start value in the following order:

·         Value in parameter file

·         Value saved in the repository

·         Initial value

·         Default value

Current Value:

The current value is the value of the variable as the session progresses. When a session starts, the current value of a variable is the same as the start value. The final current value for a variable is saved to the repository at the end of a successful session. When a session fails to complete, the Integration Service does not update the value of the variable in the repository.

Note: If a variable function is not used to calculate the current value of a mapping variable, the start value of the variable is saved to the repository.

Variable Data type and Aggregation Type When we declare a mapping variable in a mapping, we need to configure the Data type and aggregation type for the variable. The IS uses the aggregate type of a Mapping variable to determine the final current value of the mapping variable.

Aggregation types are:

 Count: Integer and small integer data types are valid only.

 Max: All transformation data types except binary data type are valid.

 Min: All transformation data types except binary data type are valid.

Variable Functions

Variable functions determine how the Integration Service calculates the current value of a mapping variable in a pipeline.

SetMaxVariable: Sets the variable to the maximum value of a group of values. It ignores rows marked for update, delete, or reject. Aggregation type set to Max.

SetMinVariable: Sets the variable to the minimum value of a group of values. It ignores rows marked for update, delete, or reject. Aggregation type set to Min.

SetCountVariable: Increments the variable value by one. It adds one to the variable value when a row is marked for insertion, and subtracts one when the row is Marked for deletion. It ignores rows marked for update or reject. Aggregation type set to Count.

SetVariable: Sets the variable to the configured value. At the end of a session, it compares the final current value of the variable to the start value of the variable. Based on the aggregate type of the variable, it saves a final value to the repository.

Creating Mapping Parameters and Variables

·         Open the folder where we want to create parameter or variable.

·         In the Mapping Designer, click Mappings > Parameters and Variables. -or- In the Mapplet Designer, click Mapplet > Parameters and Variables.

·         Click the add button.

·         Enter name. Do not remove $$ from name.

·         Select Type and Data type. Select Aggregation type for mapping variables.

·         Give Initial Value. Click ok.

·         Example: Use of Mapping of Mapping Parameters and Variables

·         EMP will be source table.

Create a target table MP_MV_EXAMPLE having columns: EMPNO, ENAME, DEPTNO, TOTAL_SAL, MAX_VAR, MIN_VAR, COUNT_VAR and SET_VAR.

TOTAL_SAL = SAL+ COMM + $$BONUS (Bonus is mapping parameter that changes every month)

SET_VAR: We will be added one month to the HIREDATE of every employee.

Create shortcuts as necessary.

Creating Mapping

1.   Open folder where we want to create the mapping.

2.   Click Tools -> Mapping Designer.

3.   Click Mapping-> Create-> Give name. Ex: m_mp_mv_example

4.   Drag EMP and target table.

5.   Transformation -> Create -> Select Expression for list -> Create –>  Done.

6.   Drag EMPNO, ENAME, HIREDATE, SAL, COMM and DEPTNO to Expression.

7.   Create Parameter $$Bonus and Give initial value as 200.

8.   Create variable $$var_max of MAX aggregation type and initial value 1500.

9.   Create variable $$var_min of MIN aggregation type and initial value 1500.

10.                Create variable $$var_count of COUNT aggregation type and initial value 0. COUNT is visible when datatype is INT or SMALLINT.

11.                Create variable $$var_set of MAX aggregation type.

12. Create 5 output ports out_ TOTAL_SAL, out_MAX_VAR, out_MIN_VAR,

out_COUNT_VAR and out_SET_VAR.

13. Open expression editor for TOTAL_SAL. Do the same as we did earlier for SAL+ COMM. To add $$BONUS to it, select variable tab and select the parameter from mapping parameter. SAL + COMM + $$Bonus

14. Open Expression editor for out_max_var.

15. Select the variable function SETMAXVARIABLE from left side pane. Select

$$var_max from variable tab and SAL from ports tab as shown below. SETMAXVARIABLE($$var_max,SAL)

17. Open Expression editor for out_min_var and write the following expression:

SETMINVARIABLE($$var_min,SAL). Validate the expression.

18. Open Expression editor for out_count_var and write the following expression:

SETCOUNTVARIABLE($$var_count). Validate the expression.

19. Open Expression editor for out_set_var and write the following expression:

SETVARIABLE($$var_set,ADD_TO_DATE(HIREDATE,'MM',1)). Validate.

20. Click OK. Expression Transformation below:

21. Link all ports from expression to target and Validate Mapping and Save it.

22. See mapping picture on next page.

PARAMETER FILE

A parameter file is a list of parameters and associated values for a workflow, worklet, or session.

Parameter files provide flexibility to change these variables each time we run a workflow or session.

We can create multiple parameter files and change the file we use for a session or workflow. We can create a parameter file using a text editor such as WordPad or Notepad.

Enter the parameter file name and directory in the workflow or session properties.

A parameter file contains the following types of parameters and variables:

Workflow variable: References values and records information in a workflow.

Worklet variable: References values and records information in a worklet. Use predefined worklet variables in a parent workflow, but we cannot use workflow variables from the parent workflow in a worklet.

Session parameter: Defines a value that can change from session to session, such as a database connection or file name.

Mapping parameter and Mapping variable

USING A PARAMETER FILE

Parameter files contain several sections preceded by a heading. The heading identifies the Integration Service, Integration Service process, workflow, worklet, or session to which we want to assign parameters or variables.

·         Make session and workflow.

·         Give connection information for source and target table.

·         Run workflow and see result.

Sample Parameter File for Our example:

In the parameter file, folder and session names are case sensitive.

Create a text file in notepad with name Para_File.txt

[Practice.ST:s_m_MP_MV_Example]

$$Bonus=1000

$$var_max=500

$$var_min=1200

$$var_count=0

CONFIGURING PARAMTER FILE

We can specify the parameter file name and directory in the workflow or session properties.

To enter a parameter file in the workflow properties:

1. Open a Workflow in the Workflow Manager.

2. Click Workflows > Edit.

3. Click the Properties tab.

4. Enter the parameter directory and name in the Parameter Filename field.

5. Click OK.

To enter a parameter file in the session properties:

1. Open a session in the Workflow Manager.

2. Click the Properties tab and open the General Options settings.

3. Enter the parameter directory and name in the Parameter Filename field.

4. Example: D:\Files\Para_File.txt or $PMSourceFileDir\Para_File.txt

5. Click OK.

Mapplet

A mapplet is a reusable object that we create in the Mapplet Designer.

It contains a set of transformations and lets us reuse that transformation logic in multiple mappings.

Created in Mapplet Designer in Designer Tool.

We need to use same set of 5 transformations in say 10 mappings. So instead of making 5 transformations in every 10 mapping, we create a mapplet of these 5 transformations. Now we use this mapplet in all 10 mappings. Example: To create a surrogate key in target. We create a mapplet using a stored procedure to create Primary key for target table. We give target table name and key column name as input to mapplet and get the Surrogate key as output.

Mapplets help simplify mappings in the following ways:

Include source definitions: Use multiple source definitions and source qualifiers to provide source data for a mapping.

Accept data from sources in a mapping

Include multiple transformations: As many transformations as we need.

Pass data to multiple transformations: We can create a mapplet to feed data to multiple transformations. Each Output transformation in a mapplet represents one output group in a mapplet.

Contain unused ports: We do not have to connect all mapplet input and output ports in a mapping.

Mapplet Input:

Mapplet input can originate from a source definition and/or from an Input transformation in the mapplet. We can create multiple pipelines in a mapplet.

We use Mapplet Input transformation to give input to mapplet.

Use of Mapplet Input transformation is optional.

Mapplet Output:

The output of a mapplet is not connected to any target table.

We must use Mapplet Output transformation to store mapplet output.

A mapplet must contain at least one Output transformation with at least one connected port in the mapplet.

Example1: We will join EMP and DEPT table. Then calculate total salary. Give the output to mapplet out transformation.

· EMP and DEPT will be source tables.

· Output will be given to transformation Mapplet_Out.

Steps:

·         Open folder where we want to create the mapping.

·         Click Tools -> Mapplet Designer.

·         Click Mapplets-> Create-> Give name. Ex: mplt_example1

·         Drag EMP and DEPT table.

·         Use Joiner transformation as described earlier to join them.

·         Transformation -> Create -> Select Expression for list -> Create -> Done

·         Pass all ports from joiner to expression and then calculate total salary as described in expression transformation.

·         Now Transformation -> Create -> Select Mapplet Out from list –> Create -> Give name and then done.

·         Pass all ports from expression to Mapplet output.

·         Mapplet -> Validate

·         Repository -> Save

Use of mapplet in mapping:

We can mapplet in mapping by just dragging the mapplet from mapplet folder on left pane as we drag source and target tables.

When we use the mapplet in a mapping, the mapplet object displays only the ports from the Input and Output transformations. These are referred to as the mapplet input and mapplet output ports.

Make sure to give correct connection information in session.

Making a mapping: We will use mplt_example1, and then create a filter

transformation to filter records whose Total Salary is >= 1500.

·         mplt_example1 will be source.

·         Create target table same as Mapplet_out transformation as in picture above. Creating Mapping

Open folder where we want to create the mapping.

·         Click Tools -> Mapping Designer.

·         Click Mapping-> Create-> Give name. Ex: m_mplt_example1

·         Drag mplt_Example1 and target table.

·         Transformation -> Create -> Select Filter for list -> Create -> Done.

·         Drag all ports from mplt_example1 to filter and give filter condition.

·         Connect all ports from filter to target. We can add more transformations after filter if needed.

·         Validate mapping and Save it.

Make session and workflow.

Give connection information for mapplet source tables.

Give connection information for target table.

Run workflow and see result.

Indirect Loading For Flat Files

Suppose, you have 10 flat files of same structure. All the flat files have same number of columns and data type. Now we need to transfer all the 10 files to same target.

Names of files are say EMP1, EMP2 and so on.

Solution1:

1. Import one flat file definition and make the mapping as per need.

2. Now in session give the Source File name and Source File Directory location of one file.

3. Make workflow and run.

4. Now open session after workflow completes. Change the Filename and Directory to give information of second file. Run workflow again.

5. Do the above for all 10 files.

Solution2:

1. Import one flat file definition and make the mapping as per need.

2. Now in session give the Source Directory location of the files.

3. Now in Fieldname use $InputFileName. This is a session parameter.

4. Now make a parameter file and give the value of $InputFileName.

$InputFileName=EMP1.txt

5. Run the workflow

6. Now edit parameter file and give value of second file. Run workflow again.

7. Do same for remaining files.

Solution3:

1. Import one flat file definition and make the mapping as per need.

2. Now make a notepad file that contains the location and name of each 10 flat files.

Sample:

D:\EMP1.txt

E:\EMP2.txt

E:\FILES\DWH\EMP3.txt and so on

3. Now make a session and in Source file name and Source File Directory location fields, give the name and location of above created file.

4. In Source file type field, select Indirect.

5. Click Apply.

6. Validate Session

7. Make Workflow. Save it to repository and run.

Incremental Aggregation

When we enable the session option-> Incremental Aggregation the Integration Service performs incremental aggregation, it passes source data through the mapping and uses historical cache data to perform aggregation calculations incrementally.

When using incremental aggregation, you apply captured changes in the source to aggregate calculations in a session. If the source changes incrementally and you can capture changes, you can configure the session to process those changes. This allows the Integration Service to update the target incrementally, rather than forcing it to process the entire source and recalculate the same data each time you run the session.

For example, you might have a session using a source that receives new data every day. You can capture those incremental changes because you have added a filter condition to the mapping that removes pre-existing data from the flow of data. You then enable incremental aggregation.

When the session runs with incremental aggregation enabled for the first time on March 1, you use the entire source. This allows the Integration Service to read and store the necessary aggregate data. On March 2, when you run the session again, you filter out all the records except those time-stamped March 2. The Integration Service then processes the new data and updates the target accordingly. Consider using incremental aggregation in the following circumstances:

You can capture new source data. Use incremental aggregation when you can capture new source data each time you run the session. Use a Stored Procedure or Filter transformation to process new data.

Incremental changes do not significantly change the target. Use incremental aggregation when the changes do not significantly change the target. If processing the incrementally changed source alters more than half the existing target, the session may not benefit from using incremental aggregation. In this case, drop the table and recreate the target with complete source data.

Note: Do not use incremental aggregation if the mapping contains percentile or median functions. The Integration Service uses system memory to process these functions in addition to the cache memory you configure in the session properties. As a result, the Integration Service does not store incremental aggregation values for percentile and median functions in disk caches.

Integration Service Processing for Incremental Aggregation

(i)The first time you run an incremental aggregation session, the Integration Service processes the entire source. At the end of the session, the Integration Service stores aggregate data from that session run in two files, the index file and the data file. The Integration Service creates the files in the cache directory specified in the Aggregator transformation properties.

(ii)Each subsequent time you run the session with incremental aggregation, you use the incremental source changes in the session. For each input record, the Integration Service checks historical information in the index file for a corresponding group. If it finds a corresponding group, the Integration Service performs the aggregate operation incrementally, using the aggregate data for that group, and saves the incremental change. If it does not find a corresponding group, the Integration Service creates a new group and saves the record data.

(iii)When writing to the target, the Integration Service applies the changes to the existing target. It saves modified aggregate data in the index and data files to be used as historical data the next time you run the session.

(iv) If the source changes significantly and you want the Integration Service to continue saving aggregate data for future incremental changes, configure the Integration Service to overwrite existing aggregate data with new aggregate data.

Each subsequent time you run a session with incremental aggregation, the Integration Service creates a backup of the incremental aggregation files. The cache directory for the Aggregator transformation must contain enough disk space for two sets of the files.

(v)When you partition a session that uses incremental aggregation, the Integration Service creates one set of cache files for each partition.

The Integration Service creates new aggregate data, instead of using historical data, when you perform one of the following tasks:

·         Save a new version of the mapping.

·         Configure the session to reinitialize the aggregate cache.

·         Move the aggregate files without correcting the configured path or directory for the files in the session properties.

·         Change the configured path or directory for the aggregate files without moving the files to the new location.

·         Delete cache files.

·         Decrease the number of partitions.

·         When the Integration Service rebuilds incremental aggregation files, the data in the previous files is lost.

Note: To protect the incremental aggregation files from file corruption or disk failure, periodically back up the files.

Preparing for Incremental Aggregation:

When you use incremental aggregation, you need to configure both mapping and session properties:

·         Implement mapping logic or filter to remove pre-existing data.

·         Configure the session for incremental aggregation and verify that the file directory has enough disk space for the aggregate files.

Configuring the Mapping

Before enabling incremental aggregation, you must capture changes in source data. You can use a Filter or Stored Procedure transformation in the mapping to remove pre-existing source data during a session.

Configuring the Session

Use the following guidelines when you configure the session for incremental aggregation:

(i) Verify the location where you want to store the aggregate files.

The index and data files grow in proportion to the source data. Be sure the cache directory has enough disk space to store historical data for the session.

When you run multiple sessions with incremental aggregation, decide where you want the files stored. Then, enter the appropriate directory for the process variable, $PMCacheDir, in the Workflow Manager. You can enter session-specific directories for the index and data files. However, by using the process variable for all sessions using incremental aggregation, you can easily change the cache directory when necessary by changing $PMCacheDir.

Changing the cache directory without moving the files causes the Integration Service to reinitialize the aggregate cache and gather new aggregate data.

In a grid, Integration Services rebuild incremental aggregation files they cannot find. When an Integration Service rebuilds incremental aggregation files, it loses aggregate history.

(ii) Verify the incremental aggregation settings in the session properties.

You can configure the session for incremental aggregation in the Performance settings on the Properties tab.

You can also configure the session to reinitialize the aggregate cache. If you choose to reinitialize the cache, the Workflow Manager displays a warning indicating the Integration Service overwrites the existing cache and a reminder to clear this option after running the session.

When should we go for hash partitioning?

Scenarios for choosing hash partitioning:
• Not enough knowledge about how much data maps into a give range.
• Sizes of range partition differ quite substantially, or are difficult to balance manually
• Range partitioning would cause data to be clustered undesirably.
• Features such as parallel DML, partition pruning, joins etc are important.

You Can Define Following Partition Types In Workflow Manager:

1) Database Partitioning
The integration service queries the IBM db2 or oracle system for table partition information. It reads partitioned data from the corresponding nodes in the database. Use database partitioning with oracle or IBM db2 source instances on a multi-node table space. Use database partitioning with db2 targets

2) Hash Partitioning
Use hash partitioning when you want the integration service to distribute rows to the partitions by group. For example, you need to sort items by item id, but you do not know how many items have a particular id number

3) Key Range
you specify one or more ports to form a compound partition key. The integration service passes data to each partition depending on the ranges you specify for each port. Use key range partitioning where the sources or targets in the pipeline are partitioned by key range.

4) Simple Pass-Through
The integration service passes all rows at one partition point to the next partition point without redistributing them. Choose pass-through partitioning where you want to create an additional pipeline stage to improve performance, but do not want to change the distribution of data across partitions

5) Round-Robin
The integration service distributes data evenly among all partitions. Use round-robin partitioning where you want each partition to process approximately the same number of rows.

Partition Types Overview

Creating Partition Tables

To create a partition table gives the following statement

Create table sales (year number(4),

                    product varchar2(10),

                   amt number(10))

                   partition by range (year)

     (

          partition p1 values less than (1992) ,

          partition p2 values less than (1993),

          partition p5 values less than (MAXVALUE)

    );

The following example creates a table with list partitioning

Create table customers (custcode number(5),

                  Name varchar2(20),

                  Addr varchar2(10,2),

                  City varchar2(20),

                  Bal number(10,2))

     Partition by list (city),

Partition north_India values (‘DELHI’,’CHANDIGARH’),

Partition east_India values (‘KOLKOTA’,’PATNA’),

Partition south_India values (‘HYDERABAD’,’BANGALORE’,

                               ’CHENNAI’),

Partition west India values (‘BOMBAY’,’GOA’);

alter table sales add partition p6 values less than (1996);

alter table customers add partition central_India values (‘BHOPAL’,’NAGPUR’);SSS

Alter table sales drop partition p5;

Alter table sales merge partition p2 and p3 into

partition p23;

The following statement adds a new set of cities ('KOCHI', 'MANGALORE') to an existing partition list.

ALTER TABLE customers

   MODIFY PARTITION south_india

      ADD VALUES ('KOCHI', 'MANGALORE');

The statement below drops a set of cities (‘KOCHI' and 'MANGALORE') from an existing partition value list.

ALTER TABLE customers

   MODIFY PARTITION south_india

      DROP VALUES (‘KOCHI’,’MANGALORE’);

SPLITTING PARTITIONS

You can split a single partition into two partitions. For example to split the partition p5 of sales table into two partitions give the following command.

Alter table sales split partition p5 into 

  (Partition p6 values less than (1996),

   Partition p7 values less then (MAXVALUE));

TRUNCATING PARTITON 

Truncating a partition will delete all rows from the partition. 

To truncate a partition give the following statement

Alter table sales truncate partition p5;

LISTING INFORMATION ABOUT PARTITION TABLES

To see how many partitioned tables are there in your schema give the following statement

Select * from user_part_tables;

To see on partition level partitioning information

Select * from user_tab_partitions;

TASKS

The Workflow Manager contains many types of tasks to help you build workflows and worklets. We can create reusable tasks in the Task Developer.

Types of tasks:

Task Type	Tool where task can be created	Reusable or not
Session	Task Developer	Yes
Email	Workflow Designer	Yes
Command	Worklet Designer	Yes
Event-Raise	Workflow Designer	No
Event-Wait	Worklet Designer	No
Timer		No
Decision		No
Assignment		No
Control		No

SESSION TASK

A session is a set of instructions that tells the Power Center Server how and when to move data from sources to targets.

To run a session, we must first create a workflow to contain the Session task.

We can run as many sessions in a workflow as we need. We can run the Session tasks sequentially or concurrently, depending on our needs.

The Power Center Server creates several files and in-memory caches depending on the transformations and options used in the session.

EMAIL TASK

The Workflow Manager provides an Email task that allows us to send email during a workflow.

Created by Administrator usually and we just drag and use it in our mapping.

Steps:

1.   In the Task Developer or Workflow Designer, choose Tasks-Create.

2.   Select an Email task and enter a name for the task. Click Create.

3.   Click Done.

4.   Double-click the Email task in the workspace. The Edit Tasks dialog box appears.

5.   Click the Properties tab.

6.   Enter the fully qualified email address of the mail recipient in the Email User Name field.

7.   Enter the subject of the email in the Email Subject field. Or, you can leave this field blank.

8.   Click the Open button in the Email Text field to open the Email Editor.

9.   Click OK twice to save your changes.

Example: To send an email when a session completes:

Steps:

1.   Create a workflow wf_sample_email

2.   Drag any session task to workspace.

3.   Edit Session task and go to Components tab.

4.   See On Success Email Option there and configure it.

5.   In Type select reusable or Non-reusable.

6.   In Value, select the email task to be used.

7.   Click Apply -> Ok.

8.   Validate workflow and Repository -> Save

9.   We can also drag the email task and use as per need.

10.                We can set the option to send email on success or failure in components tab of a session task.

COMMAND TASK

The Command task allows us to specify one or more shell commands in UNIX or DOS commands in Windows to run during the workflow.

For example, we can specify shell commands in the Command task to delete reject files, copy a file, or archive target files.

Ways of using command task:

1. Standalone Command task: We can use a Command task anywhere in the workflow or worklet to run shell commands.

2. Pre- and post-session shell command: We can call a Command task as the pre- or post-session shell command for a Session task. This is done in COMPONENTS TAB of a session. We can run it in Pre-Session Command or Post Session Success Command or Post Session Failure Command. Select the Value and Type option as we did in Email task.

Example: to copy a file sample.txt from D drive to E.

Command: COPY D:\sample.txt E:\ in windows

Steps for creating command task:

1.   In the Task Developer or Workflow Designer, choose Tasks-Create.

2.   Select Command Task for the task type.

3.   Enter a name for the Command task. Click Create. Then click done.

4.   Double-click the Command task. Go to commands tab.

5.   In the Commands tab, click the Add button to add a command.

6.   In the Name field, enter a name for the new command.

7.   In the Command field, click the Edit button to open the Command Editor.

8.   Enter only one command in the Command Editor.

9.   Click OK to close the Command Editor.

10.                Repeat steps 5-9 to add more commands in the task.

11.                Click OK.

Steps to create the workflow using command task:

1.   Create a task using the above steps to copy a file in Task Developer.

2.   Open Workflow Designer. Workflow -> Create -> Give name and click ok.

3.   Start is displayed. Drag session say s_m_Filter_example and command task.

4.   Link Start to Session task and Session to Command Task.

5.   Double click link between Session and Command and give condition in editor as

6.   $S_M_FILTER_EXAMPLE.Status=SUCCEEDED

7.   Workflow-> Validate

8.   Repository –> Save

WORKING WITH EVENT TASKS

We can define events in the workflow to specify the sequence of task execution.

Types of Events:

Pre-defined event: A pre-defined event is a file-watch event. This event Waits for a specified file to arrive at a given location.

User-defined event: A user-defined event is a sequence of tasks in the Workflow. We create events and then raise them as per need.

Steps for creating User Defined Event:

1.   Open any workflow where we want to create an event.

2.   Click Workflow-> Edit -> Events tab.

3.   Click to Add button to add events and give the names as per need.

4.   Click Apply -> Ok. Validate the workflow and Save it.

Types of Events Tasks:

EVENT RAISE: Event-Raise task represents a user-defined event. We use this task to raise a user defined event.

EVENT WAIT: Event-Wait task waits for a file watcher event or user defined event to occur before executing the next session in the workflow.

Example1: Use an event wait task and make sure that session s_filter_example runs when abc.txt file is present in D:\FILES folder.

Steps for creating workflow:

1.   Workflow -> Create -> Give name wf_event_wait_file_watch -> Click ok.

2.   Task -> Create -> Select Event Wait. Give name. Click create and done.

3.   Link Start to Event Wait task.

4.   Drag s_filter_example to workspace and link it to event wait task.

5.   Right click on event wait task and click EDIT -> EVENTS tab.

6.   Select Pre Defined option there. In the blank space, give directory and filename to watch. Example: D:\FILES\abc.tct

7.   Workflow validate and Repository Save.

Example 2: Raise a user defined event when session s_m_filter_example succeeds. Capture this event in event wait task and run session S_M_TOTAL_SAL_EXAMPLE

Steps for creating workflow:

1.   Workflow -> Create -> Give name wf_event_wait_event_raise -> Click ok.

2.   Workflow -> Edit -> Events Tab and add events EVENT1 there.

3.   Drag s_m_filter_example and link it to START task.

4.   Click Tasks -> Create -> Select EVENT RAISE from list. Give name

5.   ER_Example. Click Create and then done. Link ER_Example to s_m_filter_example.

6.   Right click ER_Example -> EDIT -> Properties Tab -> Open Value for User Defined Event and Select EVENT1 from the list displayed. Apply -> OK.

7.   Click link between ER_Example and s_m_filter_example and give the condition $S_M_FILTER_EXAMPLE.Status=SUCCEEDED

8.   Click Tasks -> Create -> Select EVENT WAIT from list. Give name EW_WAIT. Click Create and then done.

9.   Link EW_WAIT to START task.

10.                Right click EW_WAIT -> EDIT-> EVENTS tab.

11.                Select User Defined there. Select the Event1 by clicking Browse Events button.

12.                Apply -> OK.

13.                Drag S_M_TOTAL_SAL_EXAMPLE and link it to EW_WAIT.

14.                Mapping -> Validate

15.                Repository -> Save.

Run workflow and see.

TIMER TASK

The Timer task allows us to specify the period of time to wait before the Power Center Server runs the next task in the workflow. The Timer task has two types of settings:

Absolute time: We specify the exact date and time or we can choose a user-defined workflow variable to specify the exact time. The next task in workflow will run as per the date and time specified.

Relative time: We instruct the Power Center Server to wait for a specified period of time after the Timer task, the parent workflow, or the top-level workflow starts.

Example: Run session s_m_filter_example relative to 1 min after the timer task.

Steps for creating workflow:

1.   Workflow -> Create -> Give name wf_timer_task_example -> Click ok.

2.   Click Tasks -> Create -> Select TIMER from list. Give name TIMER_Example. Click Create and then done.

3.   Link TIMER_Example to START task.

4.   Right click TIMER_Example-> EDIT -> TIMER tab.

5.   Select Relative Time Option and Give 1 min and Select ‘From start time of this task’ Option.

6.   Apply -> OK.

7.   Drag s_m_filter_example and link it to TIMER_Example.

8.   Workflow-> Validate and Repository -> Save.

DECISION TASK

The Decision task allows us to enter a condition that determines the execution of the workflow, similar to a link condition.

The Decision task has a pre-defined variable called $Decision_task_name.condition that represents the result of the decision condition.

The Power Center Server evaluates the condition in the Decision task and sets the pre-defined condition variable to True (1) or False (0).

We can specify one decision condition per Decision task.

Example: Command Task should run only if either s_m_filter_example or

S_M_TOTAL_SAL_EXAMPLE succeeds. If any of s_m_filter_example or

S_M_TOTAL_SAL_EXAMPLE fails then S_m_sample_mapping_EMP should run.

Steps for creating workflow:

1.   Workflow -> Create -> Give name wf_decision_task_example -> Click ok.

2.   Drag s_m_filter_example and S_M_TOTAL_SAL_EXAMPLE to workspace and link both of them to START task.

3.   Click Tasks -> Create -> Select DECISION from list. Give name DECISION_Example. Click Create and then done. Link DECISION_Example to both s_m_filter_example and S_M_TOTAL_SAL_EXAMPLE.

4.   Right click DECISION_Example-> EDIT -> GENERAL tab.

5.   Set ‘Treat Input Links As’ to OR. Default is AND. Apply and click OK.

6.   Now edit decision task again and go to PROPERTIES Tab. Open the Expression editor by clicking the VALUE section of Decision Name attribute and enter the following condition: $S_M_FILTER_EXAMPLE.Status = SUCCEEDED OR $S_M_TOTAL_SAL_EXAMPLE.Status = SUCCEEDED

7.   Validate the condition -> Click Apply -> OK.

8.   Drag command task and S_m_sample_mapping_EMP task to workspace and link them to DECISION_Example task.

9.   Double click link between S_m_sample_mapping_EMP & DECISION_Example & give the condition: $DECISION_Example.Condition = 0. Validate & click OK.

10.                Double click link between Command task and DECISION_Example and give the condition: $DECISION_Example.Condition = 1. Validate and click OK.

11.                Workflow Validate and repository Save.

Run workflow and see the result.

CONTROL TASK

We can use the Control task to stop, abort, or fail the top-level workflow or the parent workflow based on an input link condition.

A parent workflow or worklet is the workflow or worklet that contains the Control task.

We give the condition to the link connected to Control Task.

Control Option	Description
Fail Me	Fails the control task.
Fail Parent	Marks the status of the WF or worklet that contains the Control task as failed.
Stop Parent	Stops the WF or worklet that contains the Control task.
Abort Parent	Aborts the WF or worklet that contains the Control task.
Fail Top-Level WF	Fails the workflow that is running.
Stop Top-Level WF	Stops the workflow that is running.
Abort Top-Level WF	Aborts the workflow that is running.

Example: Drag any 3 sessions and if anyone fails, then Abort the top level workflow.

Steps for creating workflow:

1.   Workflow -> Create -> Give name wf_control_task_example -> Click ok.

2.   Drag any 3 sessions to workspace and link all of them to START task.

3.   Click Tasks -> Create -> Select CONTROL from list. Give name cntr_task.

4.   Click Create and then done.

5.   Link all sessions to the control task cntr_task.

6.   Double click link between cntr_task and any session say s_m_filter_example and give the condition: $S_M_FILTER_EXAMPLE.Status = SUCCEEDED.

7.   Repeat above step for remaining 2 sessions also.

8.   Right click cntr_task-> EDIT -> GENERAL tab. Set ‘Treat Input Links As’ to OR. Default is AND.

9.   Go to PROPERTIES tab of cntr_task and select the value ‘Fail top level

10.                Workflow’ for Control Option. Click Apply and OK.

11.                Workflow Validate and repository Save.

Run workflow and see the result.

ASSIGNMENT TASK

The Assignment task allows us to assign a value to a user-defined workflow variable.

See Workflow variable topic to add user defined variables.

·         To use an Assignment task in the workflow, first create and add the

·         Assignment task to the workflow. Then configure the Assignment task to assign values or expressions to user-defined variables.

·         We cannot assign values to pre-defined workflow.

Steps to create Assignment Task:

1.   Open any workflow where we want to use Assignment task.

2.   Edit Workflow and add user defined variables.

3.   Choose Tasks-Create. Select Assignment Task for the task type.

4.   Enter a name for the Assignment task. Click Create. Then click done.

5.   Double-click the Assignment task to open the Edit Task dialog box.

6.   On the Expressions tab, click Add to add an assignment.

7.   Click the Open button in the User Defined Variables field.

8.   Select the variable for which you want to assign a value. Click OK.

9.   Click the Edit button in the Expression field to open the Expression Editor.

10.                Enter the value or expression you want to assign.

11.                Repeat steps 7-10 to add more variable assignments as necessary.

12.                Click OK.

Scheduler

We can schedule a workflow to run continuously, repeat at a given time or interval, or we can manually start a workflow. The Integration Service runs a scheduled workflow as configured.

By default, the workflow runs on demand. We can change the schedule settings by editing the scheduler. If we change schedule settings, the Integration Service reschedules the workflow according to the new settings.

• A scheduler is a repository object that contains a set of schedule settings.
• Scheduler can be non-reusable or reusable.
• The Workflow Manager marks a workflow invalid if we delete the scheduler associated with the workflow.
• If we choose a different Integration Service for the workflow or restart the Integration Service, it reschedules all workflows.
• If we delete a folder, the Integration Service removes workflows from the schedule.
• The Integration Service does not run the workflow if:
• The prior workflow run fails.
• We remove the workflow from the schedule
• The Integration Service is running in safe mode

Creating a Reusable Scheduler

• For each folder, the Workflow Manager lets us create reusable schedulers so we can reuse the same set of scheduling settings for workflows in the folder.
• Use a reusable scheduler so we do not need to configure the same set of scheduling settings in each workflow.
• When we delete a reusable scheduler, all workflows that use the deleted scheduler becomes invalid. To make the workflows valid, we must edit them and replace the missing scheduler.

Steps:

1. Open the folder where we want to create the scheduler.
2. In the Workflow Designer, click Workflows > Schedulers.
3. Click Add to add a new scheduler.
4. In the General tab, enter a name for the scheduler.
5. Configure the scheduler settings in the Scheduler tab.
6. Click Apply and OK.

Configuring Scheduler Settings

Configure the Schedule tab of the scheduler to set run options, schedule options, start options, and end options for the schedule.

There are 3 run options:

1. Run on Demand
2. Run Continuously
3. Run on Server initialization

1. Run on Demand:

Integration Service runs the workflow when we start the workflow manually.

2. Run Continuously:

Integration Service runs the workflow as soon as the service initializes. The Integration Service then starts the next run of the workflow as soon as it finishes the previous run.

3. Run on Server initialization

Integration Service runs the workflow as soon as the service is initialized. The Integration Service then starts the next run of the workflow according to settings in Schedule Options.

Schedule options for Run on Server initialization:

•  Run Once: To run the workflow just once.
•  Run every: Run the workflow at regular intervals, as configured.
•  Customized Repeat: Integration Service runs the workflow on the dates and times specified in the Repeat dialog box.

Start options for Run on Server initialization:

·         Start Date

·         Start Time

End options for Run on Server initialization:

• End on: IS stops scheduling the workflow in the selected date.
• End After: IS stops scheduling the workflow after the set number of
• Workflow runs.
• Forever: IS schedules the workflow as long as the workflow does not fail.

Creating a Non-Reusable Scheduler

1. In the Workflow Designer, open the workflow.
2. Click Workflows > Edit.
3. In the Scheduler tab, choose Non-reusable. Select Reusable if we want to select an existing reusable scheduler for the workflow.
4. Note: If we do not have a reusable scheduler in the folder, we must
5. Create one before we choose Reusable.
6. Click the right side of the Scheduler field to edit scheduling settings for the non- reusable scheduler
7. If we select Reusable, choose a reusable scheduler from the Scheduler
8. Browser dialog box.
9. Click Ok.

Points to Ponder:

• To remove a workflow from its schedule, right-click the workflow in the Navigator window and choose Unscheduled Workflow.
• To reschedule a workflow on its original schedule, right-click the workflow in the Navigator window and choose Schedule Workflow.
  

Pushdown Optimization Overview

·         You can push transformation logic to the source or target database using pushdown optimization. When you run a session configured for pushdown optimization, the Integration Service translates the transformation logic into SQL queries and sends the SQL queries to the database. The source or target database executes the SQL queries to process the transformations.

·         The amount of transformation logic you can push to the database depends on the database, transformation logic, and mapping and session configuration. The Integration Service processes all transformation logic that it cannot push to a database.

·         Use the Pushdown Optimization Viewer to preview the SQL statements and mapping logic that the Integration Service can push to the source or target database. You can also use the Pushdown Optimization Viewer to view the messages related to pushdown optimization.

·         The following figure shows a mapping containing transformation logic that can be pushed to the source database:

·         This mapping contains a Filter transformation that filters out all items except those with an ID greater than 1005. The Integration Service can push the transformation logic to the database. It generates the following SQL statement to process the transformation logic:

·         INSERT INTO ITEMS(ITEM_ID, ITEM_NAME, ITEM_DESC, n_PRICE) SELECT ITEMS.ITEM_ID, ITEMS.ITEM_NAME, ITEMS.ITEM_DESC, CAST(ITEMS.PRICE AS INTEGER) FROM ITEMS WHERE (ITEMS.ITEM_ID >1005)

·         The Integration Service generates an INSERT SELECT statement to get the ID, NAME, and DESCRIPTION columns from the source table. It filters the data using a WHERE clause. The Integration Service does not extract data from the database at this time.

Pushdown Optimization Types

You can configure the following types of pushdown optimization:

	Source-side pushdown optimization. The Integration Service pushes as much transformation logic as possible to the source database.
	Target-side pushdown optimization. The Integration Service pushes as much transformation logic as possible to the target database.

Full pushdown optimization. The Integration Service attempts to push all transformation logic to the target database. If the Integration Service cannot push all transformation logic to the database, it performs both source-side and target-side pushdown optimization.

Running Source-Side Pushdown Optimization Sessions

When you run a session configured for source-side pushdown optimization, the Integration Service analyzes the mapping from the source to the target or until it reaches a downstream transformation it cannot push to the database.

The Integration Service generates and executes a SELECT statement based on the transformation logic for each transformation it can push to the database. Then, it reads the results of this SQL query and processes the remaining transformations.

Running Target-Side Pushdown Optimization Sessions

When you run a session configured for target-side pushdown optimization, the Integration Service analyzes the mapping from the target to the source or until it reaches an upstream transformation it cannot push to the database. It generates an INSERT, DELETE, or UPDATE statement based on the transformation logic for each transformation it can push to the database. The Integration Service processes the transformation logic up to the point that it can push the transformation logic to the target database. Then, it executes the generated SQL.

Running Full Pushdown Optimization Sessions

To use full pushdown optimization, the source and target databases must be in the same relational database management system. When you run a session configured for full pushdown optimization, the Integration Service analyzes the mapping from the source to the target or until it reaches a downstream transformation it cannot push to the target database. It generates and executes SQL statements against the source or target based on the transformation logic it can push to the database.

When you run a session with large quantities of data and full pushdown optimization, the database server must run a long transaction. Consider the following database performance issues when you generate a long transaction:

	A long transaction uses more database resources.
	A long transaction locks the database for longer periods of time. This reduces database concurrency and increases the likelihood of deadlock.

A long transaction increases the likelihood of an unexpected event.

To minimize database performance issues for long transactions, consider using source-side or target-side pushdown optimization.

Integration Service Behavior with Full Optimization

When you configure a session for full optimization, the Integration Service analyzes the mapping from the source to the target or until it reaches a downstream transformation it cannot push to the target database. If the Integration Service cannot push all transformation logic to the target database, it tries to push all transformation logic to the source database. If it cannot push all transformation logic to the source or target, the Integration Service pushes as much transformation logic to the source database, processes intermediate transformations that it cannot push to any database, and then pushes the remaining transformation logic to the target database. The Integration Service generates and executes an INSERT SELECT, DELETE, or UPDATE statement for each database to which it pushes transformation logic.

For example, a mapping contains the following transformations:

The Rank transformation cannot be pushed to the source or target database. If you configure the session for full pushdown optimization, the Integration Service pushes the Source Qualifier transformation and the Aggregator transformation to the source, processes the Rank transformation, and pushes the Expression transformation and target to the target database. The Integration Service does not fail the session if it can push only part of the transformation logic to the database.

Active and Idle Databases

During pushdown optimization, the Integration Service pushes the transformation logic to one database, which is called the active database. A database that does not process transformation logic is called an idle database. For example, a mapping contains two sources that are joined by a Joiner transformation. If the session is configured for source-side pushdown optimization, the Integration Service pushes the Joiner transformation logic to the source in the detail pipeline, which is the active database. The source in the master pipeline is the idle database because it does not process transformation logic.

The Integration Service uses the following criteria to determine which database is active or idle:

	1. When using full pushdown optimization, the target database is active and the source database is idle.
	2. In sessions that contain a Lookup transformation, the source or target database is active, and the lookup database is idle.

	3. In sessions that contain a Joiner transformation, the source in the detail pipeline is active, and the source in the master pipeline is idle.
	4. In sessions that contain a Union transformation, the source in the first input group is active. The sources in other input groups are idle.

To push transformation logic to an active database, the database user account of the active database must be able to read from the idle databases.

Working with Databases

You can configure pushdown optimization for the following databases:

	IBM DB2
	Microsoft SQL Server

	Netezza
	Oracle

	Sybase ASE
	Teradata

Databases that use ODBC drivers

When you push transformation logic to a database, the database may produce different output than the Integration Service. In addition, the Integration Service can usually push more transformation logic to a database if you use a native driver, instead of an ODBC driver.

Comparing the Output of the Integration Service and Databases

The Integration Service and databases can produce different results when processing the same transformation logic. The Integration Service sometimes converts data to a different format when it reads data. The Integration Service and database may also handle null values, case sensitivity, and sort order differently.

The database and Integration Service produce different output when the following settings and conversions are different:

Nulls treated as the highest or lowest value. The Integration Service and a database can treat null values differently. For example, you want to push a Sorter transformation to an Oracle database. In the session, you configure nulls as the lowest value in the sort order. Oracle treats null values as the highest value in the sort order.

Sort order. The Integration Service and a database can use different sort orders. For example, you want to push the transformations in a session to a Microsoft SQL Server database, which is configured to use a sort order that is not case sensitive. You configure the session properties to use the binary sort order, which is case sensitive. The results differ based on whether the Integration Service or Microsoft SQL Server database process the transformation logic.

Case sensitivity. The Integration Service and a database can treat case sensitivity differently. For example, the Integration Service uses case sensitive queries and the database does not. A Filter transformation uses the following filter condition: IIF(col_varchar2 = ‘CA’, TRUE, FALSE). You need the database to return rows that match ‘CA.’ However, if you push this transformation logic to a Microsoft SQL Server database that is not case sensitive, it returns rows that match the values ‘Ca,’ ‘ca,’ ‘cA,’ and ‘CA.’

Numeric values converted to character values. The Integration Service and a database can convert the same numeric value to a character value in different formats. The database can convert numeric values to an unacceptable character format. For example, a table contains the number 1234567890. When the Integration Service converts the number to a character value, it inserts the characters ‘1234567890.’ However, a database might convert the number to ‘1.2E9.’ The two sets of characters represent the same value. However, if you require the characters in the format ‘1234567890,’ you can disable pushdown optimization.

Precision. The Integration Service and a database can have different precision for particular datatypes. Transformation datatypes use a default numeric precision that can vary from the native datatypes. For example, a transformation Decimal datatype has a precision of 1-28. The corresponding Teradata Decimal datatype has a precision of 1-18. The results can vary if the database uses a different precision than the Integration Service.

Using ODBC Drivers

When you use native drivers for all databases, except Netezza, the Integration Service generates SQL statements using native database SQL. When you use ODBC drivers, the Integration Service usually cannot detect the database type. As a result, it generates SQL statements using ANSI SQL. The Integration Service can generate more functions when it generates SQL statements using the native language than ANSI SQL.

Note: Although the Integration Service uses an ODBC driver for the Netezza database, the Integration Service detects that the database is Netezza and generates native database SQL when pushing the transformation logic to the Netezza database.

In some cases, ANSI SQL is not compatible with the database syntax. The following sections describe problems that you can encounter when you use ODBC drivers. When possible, use native drivers to prevent these problems.

Working with Dates

The Integration Service and database can process dates differently. When you configure the session to push date conversion to the database, you can receive unexpected results or the session can fail.

The database can produce different output than the Integration Service when the following date settings and conversions are different:

Date values converted to character values. The Integration Service converts the transformation Date/Time datatype to the native datatype that supports subsecond precision in the database. The session fails if you configure the datetime format in the session to a format that the database does not support. For example, when the Integration Service performs the ROUND function on a date, it stores the date value in a character column, using the format MM/DD/YYYY HH:MI:SS.US. When the database performs this function, it stores the date in the default date format for the database. If the database is Oracle, it stores the date as the default DD-MON-YY. If you require the date to be in the format MM/DD/YYYY HH:MI:SS.US, you can disable pushdown optimization.

Date formats for TO_CHAR and TO_DATE functions. The Integration Service uses the date format in the TO_CHAR or TO_DATE function when the Integration Service pushes the function to the database. The database converts each date string to a datetime value supported by the database.

For example, the Integration Service pushes the following expression to the database:

TO_DATE( DATE_PROMISED, 'MM/DD/YY' )

The database interprets the date string in the DATE_PROMISED port based on the specified date format string MM/DD/YY. The database converts each date string, such as 01/22/98, to the supported date value, such as Jan 22 1998 00:00:00.

If the Integration Service pushes a date format to an IBM DB2, a Microsoft SQL Server, or a Sybase database that the database does not support, the Integration Service stops pushdown optimization and processes the transformation.

The Integration Service converts all dates before pushing transformations to an Oracle or Teradata database. If the database does not support the date format after the date conversion, the session fails.

	HH24 date format. You cannot use the HH24 format in the date format string for Teradata. When the Integration Service generates SQL for a Teradata database, it uses the HH format string instead.
	Blank spaces in date format strings. You cannot use blank spaces in the date format string in Teradata. When the Integration Service generates SQL for a Teradata database, it substitutes the space with ‘B.’

Handling subsecond precision for a Lookup transformation. If you enable subsecond precision for a Lookup transformation, the database and Integration Service perform the lookup comparison using the subsecond precision, but return different results. Unlike the Integration Service, the database does not truncate the lookup results based on subsecond precision. For example, you configure the Lookup transformation to show subsecond precision to the millisecond. If the lookup result is 8:20:35.123456, a database returns 8:20:35.123456, but the Integration Service returns 8:20:35.123.

SYSDATE built-in variable. When you use the SYSDATE built-in variable, the Integration Service returns the current date and time for the node running the service process. However, when you push the transformation logic to the database, the SYSDATE variable returns the current date and time for the machine hosting the database. If the time zone of the machine hosting the database is not the same as the time zone of the machine running the Integration Service process, the results can vary.