IICS Tutorial_10 Union and Sequence Transformation Use Case and Properties

In Informatica Intelligent Cloud Services (IICS), the Union and Sequence transformations serve distinct purposes in data integration tasks. Here's an overview of their uses:

Union Transformation:

Combining Data: The primary purpose of the Union transformation is to combine data from multiple input sources into a single output stream. This transformation is used when you have similar data structures from different sources and want to merge them into a single dataset.
Appending Rows: The Union transformation appends rows from each input source sequentially, preserving the order of rows from each source. It doesn't remove duplicate rows by default, so if duplicate elimination is required, you may need to use a sorter or other transformations.
Parallel Processing: The Union transformation can process data from multiple sources in parallel, improving performance by distributing the workload across multiple nodes or partitions.
Sequence Transformation:

Generating Sequence Numbers: The Sequence transformation is used to generate sequence numbers or IDs for rows in a dataset. It assigns a unique numeric value to each row based on a specified sequence definition.
Ordering Data: In addition to generating sequence numbers, the Sequence transformation can also be used to order data based on the generated sequence numbers. This can be useful for enforcing a specific order in the output dataset.
Incrementing Values: The Sequence transformation can increment sequence values by a specified increment for each row processed, allowing you to control the sequence generation behavior.
Example Use Cases:

Union Transformation:
Combining sales data from multiple regional offices into a single dataset for reporting purposes.
Merging customer information from different data sources (e.g., CRM systems) into a unified customer master dataset.
Aggregating log files from multiple servers or applications into a centralized log repository.
Sequence Transformation:
Assigning unique invoice numbers to sales transactions in a data warehouse.
Generating primary keys for new records in a database table.
Creating a sequence of IDs for individual steps in an ETL workflow to track processing order.