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What are Transformation Types with examples in Informatica?
Informatica is a powerful data integration tool used to extract, transform, and load (ETL) data across various sources and destinations. Transformations are the core of Informatica’s data processing capabilities, allowing for the manipulation of data as it moves through a workflow. Informatica Online Training
Here are the main types of transformations in Informatica, along with examples:
1. Source Qualifier Transformation
• Purpose: Converts source data into a format that Informatica can use.
• Example: Extracting data from an Oracle database and converting it into a row set that Informatica can process.
2. Expression Transformation
• Purpose: Performs row-wise calculations or transformations.
• Example: Calculating the total price of an order by multiplying the quantity by the unit price. Informatica Training Institutes in Hyderabad
3. Aggregator Transformation
• Purpose: Performs aggregate calculations, such as sums, averages, counts, etc.
• Example: Calculating the total ...
... sales amount per region.
4. Filter Transformation
• Purpose: Filters rows based on a specified condition.
• Example: Selecting records where the sales amount is greater than $1000.
5. Joiner Transformation
• Purpose: Joins data from two heterogeneous sources based on a join condition.
• Example: Joining customer data from an SQL Server database with sales data from an Oracle database.
6. Lookup Transformation
• Purpose: Looks up data in a relational table, view, or synonym.
• Example: Looking up the current address of a customer from a reference table using customer ID.
7. Sorter Transformation
• Purpose: Sorts data in ascending or descending order based on specified columns.
• Example: Sorting employee data by employee ID in ascending order.
8. Router Transformation
• Purpose: Routes data into multiple groups based on specified conditions.
• Example: Routing sales data into different groups based on region.
9. Union Transformation
• Purpose: Combines data from multiple pipelines into a single pipeline.
• Example: Combining sales data from multiple regions into a single dataset.
10. Update Strategy Transformation
• Purpose: Determines how to handle updates to data in the target.
• Example: Flagging records for insertion, update, deletion, or rejection based on business logic. Informatica Cloud Data Integration Training
11. Sequence Generator Transformation
• Purpose: Generates unique numeric values.
• Example: Generating unique invoice numbers for sales transactions.
12. Stored Procedure Transformation
• Purpose: Calls and executes a stored procedure in a database.
• Example: Executing a stored procedure to update inventory levels after processing a sales order.
13. Transaction Control Transformation
• Purpose: Defines transaction boundaries within a session.
• Example: Committing data to the target after processing a certain number of rows.
14. Rank Transformation
• Purpose: Select the top or bottom-ranked data.
• Example: Identifying the top 10 salespersons based on sales amount.
15. Normalizer Transformation
• Purpose: Converts denormalized data to a normalized format.
• Example: Converting a single row with multiple repeating columns into multiple rows.
16. XML Source Qualifier Transformation
• Purpose: Processes XML data from XML sources.
• Example: Extracting and transforming customer data from an XML file.
17. SQL Transformation
• Purpose: Executes SQL queries against a database.
• Example: Performing a complex SQL join operation on two tables and integrating the result into the data flow. Informatica Cloud Online Training
18. Union Transformation
• Purpose: Combines data from multiple sources into a single pipeline.
• Example: Merging employee data from different departments into a single dataset.
19. Java Transformation
• Purpose: Executes custom Java code within a mapping.
• Example: Implementing complex business logic that is easier to code in Java than using Informatica expressions.
20. Python Transformation
• Purpose: Executes custom Python scripts within a mapping.
• Example: Performing advanced data analytics and machine learning operations.
Conclusion
Informatica transformations are powerful tools for manipulating and managing data during the ETL process. They allow for complex data integration tasks to be handled efficiently and effectively, ensuring that data is processed, cleaned, and transformed according to business requirements. Understanding and utilizing these transformations can significantly enhance the capabilities of data integration workflows. IICS Training in Hyderabad