Overview of Oracle Golden Gate Data Pumps

A data pump is a secondary Extract group within the source Oracle GoldenGate configuration. If a data pump is not used, Extract must send the captured data operations to a remote trail on the target. In a typical configuration with a data pump, however, the primary Extract group writes to a trail on the source system. The data pump reads this trail and sends the data operations over the network to a remote trail on the target. The data pump adds storage flexibility and also serves to isolate the primary Extract process from TCP/IP activity.

In general, a data pump can perform data filtering, mapping, and conversion, or it can be configured in pass through mode, where data is passively transferred as-is, without manipulation. Pass-through mode increases the throughput of the data pump, because all of the functionality that looks up object definitions is bypassed.

In most business cases, you should use a data pump. Some reasons for using a data pump include the following:

Protection against network and target failures: 

In a basic Oracle GoldenGate configuration, with only a trail on the target system, there is nowhere on the source system to store the data operations that Extract continuously extracts into memory. If the network or the target system becomes unavailable, Extract could run out of memory and abend. However, with a trail and data pump on the source system, captured data can be moved to disk, preventing the abend of the primary Extract. When connectivity is restored, the data pump captures the data from the source trail and sends it to the target system.

You are implementing several phases of data filtering or transformation. 

When using complex filtering or data transformation configurations, you can configure a data pump to perform the first transformation either on the source system or on the target system, or even on an intermediary system, and then use another data pump or the Replicat group to perform the second transformation.

Consolidating data from many sources to a central target. 

When synchronizing multiple source databases with a central target database, you can store extracted data operations on each source system and use data pumps on each of those systems to send the data to a trail on the target system. Dividing the storage load between the source and target systems reduces the need for massive amounts of space on the target system to accommodate data arriving from multiple sources.

Synchronizing one source with multiple targets.

When sending data to multiple target systems, you can configure data pumps on the source system for each target. If network connectivity to any of the targets fails, data can still be sent to the other targets.