Table of Contents

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  1. Preface
  2. Introduction to PowerExchange for Google BigQuery
  3. PowerExchange for Google BigQuery Configuration
  4. Google BigQuery Sources and Targets
  5. Google BigQuery Mappings
  6. Google BigQuery Sessions
  7. Google BigQuery as CDC Target
  8. Google BigQuery Pushdown Optimization
  9. Appendix A: Google BigQuery Data Type Reference

PowerExchange for Google BigQuery User Guide for PowerCenter

PowerExchange for Google BigQuery User Guide for PowerCenter

CDC Target Overview

CDC Target Overview

You can read the real-time or changed data from a Change Data Capture (CDC) source and load the data to Google BigQuery.
Create a PowerExchange for Google BigQuery connection to access Google BigQuery and write the data from a CDC source.
When the session processes the changed data from a CDC source such as PowerExchange Express CDC for Oracle, PowerExchange for Google BigQuery creates a state table and a staging table in Google BigQuery. When the changed data is received from the CDC source, PowerExchange for Google BigQuery 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. PowerExchange for Google BigQuery then merges the stage table with the actual target table in Google BigQuery.
Each time you run the session, PowerExchange for Google BigQuery creates the state table to store the state information. PowerExchange for Google BigQuery uses the following naming convention for the state table name:
state_tables_<first 20 characters of the workflow name OR application name specified in the CDC connection>_<session_ID>
Similarly, PowerExchange for Google BigQuery uses the following naming convention for the staging table name:
staging_table_cdc_<dataset_name>_<targetTable_name>_<session_name>

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