Source Execution Group(s)

1. What is a Source Execution Group?

• A Source Execution Group is a collection of Source Datastores that are grouped together because they either belong to the same dataset or are located on the same physical data server. These source datastores can be processed together in parallel.

2. Grouping Source Datastores:

• Same Dataset or Physical Server: Source datastores that are within the same dataset or reside on the same physical data server are automatically grouped into a single Source Execution Group. This allows them to be extracted simultaneously, which is more efficient.

3. Parallel Extraction of Datastores:

• The source execution group allows the grouped datastores to be extracted at the same time. This means that multiple source datastores can be processed concurrently, speeding up data extraction and improving performance.

4. Physical Diagram Representation:

• In the physical diagram, the Source Execution Group is displayed as part of the visual representation. The grouped source datastores will be shown together to indicate that they will be processed in parallel.

5. Benefits of Source Execution Groups:

• Efficiency: By grouping datastores that are either within the same dataset or on the same physical server, you can execute their extraction concurrently, reducing the overall processing time.
• Resource Optimization: Since the datastores are grouped by location or dataset, you make efficient use of system resources and ensure that data extraction tasks do not compete for the same resources.

6. Automatic Grouping:

• The system typically automatically groups datastores into Source Execution Groups based on their location (dataset or physical server). However, depending on your setup, you may be able to manually modify or configure groupings to match your specific needs.

7. Managing Source Execution Groups:

• You can manage the source execution groups within your physical diagram to ensure that all related data extraction tasks are grouped and executed efficiently, optimizing data processing and reducing resource contention.