You can use Google BigQuery V2 Connector to capture changed data from any CDC source and write the changed data to a Google BigQuery target. Add the CDC sources in mappings, and then run the associated mapping tasks to write the changed data to the target. When you capture changed data from a CDC source, you can only configure a single Google BigQuery V2 target transformation in a mapping. You can configure multiple Google BigQuery V2 targets to write changed data from a CDC source. You can configure multiple pipelines in a mapping to write changed data from multiple CDC sources to multiple Google BigQuery V2 targets.

When the mapping task processes the changed data from a CDC source such as Oracle Express CDC V2, Google BigQuery V2 Connector creates a state table and a staging table in Google BigQuery. When the changed data is received from the CDC source, Google BigQuery V2 Connector uploads the changed data to the staging table. Then, it generates a

Job_Id

and writes the

Job_Id

to the state table along with the restart information. Google BigQuery V2 Connector then merges the stage table with the actual target table in Google BigQuery.

Each time you run the mapping task, Google BigQuery V2 Connector creates the state table, if it does not exist, to store the state information. Google BigQuery V2 Connector uses the following naming convention for the state table name:

state_table_cdc_<MappingTaskID>_<UniqueIdentifierForTargetInstance(s)>

Similarly, Google BigQuery V2 Connector uses the following naming convention for the staging table name:

staging_table_cdc_<MappingTaskID>_<TargetInstanceName>