In the realm of Data Warehousing, the terms 'Active Stage' and 'Passive Stage' are fundamental concepts to grasp when working with DataStage.
| Active Stage | Passive Stage |
| Active stages perform actions - change data, add columns, filter rows, summarize rows, etc. | Passive stages read/write data - files, datasets, tables |
By understanding the role of both Active and Passive stages in DataStage, you can better design and implement your data warehousing solutions for optimal performance and accuracy.
In IBM InfoSphere DataStage, two primary types of process components are used: Active and Passive stages. Understanding the differences between these two can help you design more efficient ETL processes.
An Active stage is a DataStage process component that performs some transformation or aggregation of data. It reads from input queues, processes the data according to its defined logic, and then writes the results to output queues.
ActiveStageName myActiveStage;
begin myActiveStage;
-- Your transformation logic here
end myActiveStage;A Passive stage is a DataStage process component that simply copies data from one input queue to another output queue without performing any transformation or aggregation.
PassiveStageName myPassiveStage;
begin myPassiveStage;
-- No processing logic, just data copying
end myPassiveStage;| Active Stage | Passive Stage | |
|---|---|---|
| Performs Transformation/Aggregation | Yes | No |
| Reads from input queues and writes to output queues | Yes | Yes |
| Can be used in ETL processes for complex transformations | Yes | No |
Understanding the Active and Passive stages in DataStage is crucial for designing efficient ETL processes. While both components can move data between queues, only Active stages can transform or aggregate data as needed.