Introduction to Transformations in Informatica and Filter Transformation - Shikshaglobe

Content Creator: Vijay Kumar

What is Transformation?

Transformations in Informatica are the objects which create, modify or pass data to the defined target structures (tables, files, or any other target).

The purpose of the transformation in Informatica is to modify the source data as per the requirement of the target system. It also ensures the quality of the data being loaded into the target.

Informatica provides various transformations to perform specific functionalities.

For example, performing tax calculations based upon source data, data cleansing operations, etc. In transformations, we connect the ports to pass data to it, and transformation returns the output through output ports.

What Are Data Transformations?

Data transformations are the core operations performed on data during the ETL (Extract, Transform, Load) process. They involve changing the structure, format, or values of data to meet the desired output requirements.

Significance of Transformations

Transformations are vital for several reasons:

  • Data Cleansing: They help clean and validate data, ensuring its accuracy and consistency.
  • Data Enrichment: Transformations can enrich data by adding or deriving new information from existing data.
  • Data Integration: They enable the integration of data from disparate sources, harmonizing it for unified analysis.
  • Business Logic Implementation: Transformations allow the application of specific business rules and logic to the data.

Filter Transformation: Unearthing Data Precision

What Is a Filter Transformation?

A Filter Transformation is a specific type of transformation in Informatica that does exactly what its name suggests - it filters data. It's like a gatekeeper, allowing only the data that meets certain conditions or criteria to pass through while discarding the rest.

Working of Filter Transformation

Filter Transformations work based on predefined conditions, also known as filter expressions. These expressions determine which rows of data will be allowed to flow through the transformation, and which will be filtered out. This is particularly useful when you need to extract specific subsets of data from a larger dataset.

Use Cases of Filter Transformation

Filter Transformations find applications in various scenarios:

  • Data Quality Assurance: They can be used to filter out erroneous or incomplete data, ensuring that only high-quality data enters the destination.
  • Data Segmentation: Filter Transformations are handy when you want to segment data based on certain attributes or criteria.
  • Data Subset Extraction: You can use Filter Transformations to extract specific subsets of data for different business needs.

Implementing Filter Transformation in Informatica

Configuration and Customization

To utilize Filter Transformations in Informatica, you need to configure them within your mapping. You define the filter conditions using SQL-like expressions, and only rows satisfying these conditions will be included in the output.

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Performance Considerations

Efficient use of Filter Transformations is crucial for maintaining optimal performance. Overly complex filter conditions or applying them too early in the transformation process can impact performance negatively.

Classification of Transformation

Transformation is classified into two categories, one based on connectivity, and the other based on the change in the of rows. First, we will look at the transformation based on connectivity.

Types of transformation based on connectivity

Connected Transformations

Unconnected Transformations

In Informatica, during mappings, the transformations which are connected to other transformations are called connected transformations.

For example, Source qualifier transformation of Source table EMP is connected to filter transformation to filter employees of a dept.

Those transformations that are not connected to any other transformations are called unconnected transformations.

Their functionality is used by calling them inside other transformations like Expression transformation. These transformations are not part of the pipeline.

The connected transformations are preferred when for every input row, a transformation is called or is expected to return a value. For example, for the zip codes in every row, the transformation returns the city name.


The unconnected transformations are useful when their functionality is only required periodically or based upon certain conditions. For example, calculate the tax details if the tax value is not available.

Types of transformations based on the change in the rows

Active Transformations

Passive Transformations

Active Transformations are those that modify the data rows and the number of input rows passed to them. For example, if a transformation receives ten rows as input, and it returns fifteen rows as an output then it is an active transformation. The data in the row is also modified in the active transformation.


Passive transformations are those that do not change the number of input rows. In passive transformations the number of input and output rows remain the same, only data is modified at row level.

In the passive transformation, no new rows are created, or existing rows are dropped.

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Following is the List of Transformations in Informatica

Source Qualifier Transformation

Aggregator Transformation

Router Transformation

Joiner transformation

Rank Transformation

Sequence Generator Transformation

Transaction Control Transformation

Lookup and Re-usable transformation

Normalizer Transformation

Performance Tuning for Transformation

External Transformation

Expression Transformation

What is Filter Transformation?

Filter Transformation is an active transformation as it changes the no of records.


Using the filter transformation, we can filter the records based on the filter condition. Filter transformation is an active transformation as it changes the no of records.

For example, for loading the employee records having deptno equal to 10 only, we can put filter transformation in the mapping with the filter condition deptno=10. So only those records which have deptno =10 will be passed by filter transformation, the rest other records will be dropped.


How to use filter transformation-

Step 1 – Create a mapping having source “EMP” and target “EMP_TARGET”

Step 2 – Then in the mapping


Select Transformation menu

Step 3 – Then in the creative transformation window

Learn more

Select Filter Transformation from the list

Enter Transformation name “fltr_deptno_10”

Step 4 – The filter transformation will be created, select the “Done” button in the creative transformation window

Step 5 – In the mapping

Drag and drop all the Source qualifier columns to the filter transformation

Link the columns from filter transformation to the target table

Click here for details

Step 6 – Double click on the filter transformation to open its properties, and then


Select the properties menu

Click on the Filter condition editor


Step 7 – Then in the filter condition expression editor

Enter filter condition – deptno=10

Select OK button

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Step 8 – Now again in the edit transformation window in the Properties tab you will see the filter condition, select the OK button

Now save the mapping and execute it after creating the session and workflow. In the target table, the records having deptno=10 only will be loaded.

In this way, you can filter the source records using filter transformation.


Read More :

Rank Transformation in Informatica
Joiner transformation in Informatica
Router Transformation in Informatica
Aggregator Transformation in Informatica

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