Learning Path for ETL Design Informatica

New to this topic? Not sure where to start? For your convenience, we have created the below learning path where articles are organised in the order of their relevance and complexity. Start from the beginning and read one by one to master the subject.

1All about Informatica LookupBasic
2Aggregation with out Informatica AggregatorIntermediate
3Implementing Informatica Persistent CacheIntermediate
4The benefit and disadvantage of Informatica Persistent Cache LookupAdvanced
5Informatica Dynamic Lookup CacheIntermediate
6What is Active Lookup TransformationBasic
7Working with Informatica FlatfilesBasic
8Loading Flatfiles delimited by comma and double quotesIntermediate
9Informatica Excel SourceBasic
10Informatica Reject File - How to Identify rejection reasonBasic
11Implementing Informatica Incremental AggregationIntermediate
12Using Informatica Stored Procedure TransformationBasic
13When to use Informatica Stored Procedure TransformationBasic
14Using Informatica Normalizer TransformationBasic
15Informatica Java TransformationBasic
16Calling C executable from Java TransformBasic
17Stop Hardcoding- Follow Parameterization TechniqueIntermediate
18CDC Implementation using Informatica VariableIntermediate
19CDC Implementation using FlatfileIntermediate
20Implementing SCD2 in Informatica Using ORA_HASH at SourceIntermediate
21Comparing Performance of SORT operation (Order By) in Informatica and OracleAdvanced
22Informatica Join Vs Database JoinAdvanced
23Informatica Tuning - Step by Step ApproachBasic
24Informatica Performance Tuning - Complete GuideIntermediate
25How to Tune Performance of Informatica Lookup TransformationAdvanced
26How to Tune Performance of Informatica Joiner TransformationAdvanced
27How to Tune Performance of Informatica Aggregator TransformationAdvanced
28Implementing Informatica PartitionsIntermediate
29Pushdown Optimization In InformaticaIntermediate
30PowerCenter SOA ComponentsBasic
31Informatica Metadata Tables - Overview and TutorialBasic
32How to get Informatica Repository Information from MetadataBasic
33How to get Folders and Mapping names from Informatica Metadata QueryBasic
34Useful Informatica Metadata Repository QueriesBasic
35APEAR - A tool for automating Informatica Performance TuningAdvanced
36Best Informatica Interview Questions & AnswersBasic

This article explains the Change Data Capture mechanism using Informatica Mapping Variable. We can use the Informatica Mapping Variable to extract the CDC data without using any other custom table. Here it goes.

This article tries to minimize hard-coding in ETL, thereby increasing flexibility, reusability, readabilty and avoides rework through the judicious use of Informatica Parameters and Variables.

We are going to do is, to call C++ Executable from Informatica, using Passive Java Transform and capture the output of the C++ using Java and write the result to corresponding target column.

Feel the Power of Java programming language to transform data in PowerCenter Informatica. Java Transformation in Informatica can be used either in Active or Passive Mode.

Normalizer transformation is a native transformation in Informatica that can ease many complex data transformation requirements. Learn how to effectively use normalizer in this tutorial.

There are loads of mis-information spreaded across Internet on good use-cases of Informatica Stored Procedure transformation. Exactly where do you use this transformation? This article finds out.

Stored Procedure Transformation - as the name suggests is used to execute stored procedures through Informatica ETL. It can also be used to call functions to return calculated values. The Stored Procedures that are to be executed should be pre-built on the database which can be connected through Informatica.

Using incremental aggregation, we apply captured changes in the source data (CDC part) to aggregate calculations in a session. If the source changes incrementally and we can capture the changes, then we can configure the session to process those changes. This allows the Integration Service to update the target incrementally, rather than forcing it to delete previous loads data, process the entire source data and recalculate the same data each time you run the session.

When we run a session, the integration service may create a reject file for each target instance in the mapping to store the target reject record. With the help of the Session Log and Reject File we can identify the cause of data rejection in the session.

This article is a guide on how to Unload data from EXCEL file system to target relational database using Informatica.

In this article let us take up a very trivial but an important aspect that we as DW developers usual face. This is related to loading flat file sources. Whenever we have flat file sources we usual ask source systems for a specific type of field delimiters.

In this article series we will try to cover all the possible scenarios related to flatfiles in Informatica.

Informatica 9x allows us to configure Lookup transformation to return multiple rows. So now we can retrieve multiple rows from a lookup table thus making Lookup transformation an Active transformation type.

A LookUp cache does not change its data once built. But what if the underlying table upon which lookup was done changes the data after the lookup cache is created? Is there a way so that the cache always remain up-to-date even if the underlying table changes?

Persistent cache may be your choice of caching when it comes to lookup performance. But you should be aware of the hazards of persistent cache as well.

You must have noticed that the "time" Informatica takes to build the lookup cache can be too much sometimes depending on the lookup table size/volume. Using Persistent Cache, you may save lot of your time. This article describes how to do it.

Since Informatica process data on row by row basis, it is generally possible to handle data aggregation operation even without an Aggregator Transformation. On certain cases, you may get huge performance gain using this technique!

A Lookup is a Passive, Connected or Unconnected Transformation used to look up data in a relational table, view, synonym or flat file. The integration service queries the lookup table to retrieve a value based on the input source value and the lookup condition.

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