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Overview

Change Data Capture (CDC) pipeline allows extracting changes in real-time from the databases which support CDC and loading them into BigQuery.

Etlworks includes a built-in, deeply integrated CDC engine based on a customized version of Debezium.

There is nothing to install or manage separately—the CDC engine runs as part of the flow execution.

Supported source databases

MySQL
SQL Server
PostgreSQL
Oracle
DB2
MongoDB
AS400 (IBMI platfroms)

When to use this pipeline

Use the pipeline described in this article to extract data from the CDC-enabled database and load it into Google BigQuery in real-time.

Flows optimized for BigQuery

Flow type		When to use
Any to BigQuery: Database to BigQuery File to BigQuery Queue to BigQuery Web service to BigQuery Well-known API to BigQuery		When you need to extract data from any source, transform it and load it into BigQuery.
Bulk load files in Google Cloud Storage into BigQuery		When you need to bulk-load files that already exist in Google Cloud Storage without applying any transformations. The flow automatically generates the COPY command and MERGEs data into the destination.
Stream CDC events into BigQuery	You are here	When you need to stream updates from the database which supports Change Data Capture (CDC) into BigQuery in real-time.
Stream messages from a queue into BigQuery		When you need to stream messages from the message queue which supports streaming into BigQuery in real time.

How it works

A CDC pipeline into BigQuery captures real-time changes from a CDC-enabled source database, stages them in GC storage and loads them using BigQuery’s native ingestion mechanisms.

You can build the pipeline using two options, depending on your reliability, performance, and scaling needs:

Option 1: Single Flow – Stream and Load Together

Use this option for simplicity and quick setup.

A single flow streams CDC events into GS storage and periodically loads them into target tables using native BigQuery bulk load (as frequently as every second).
Only one flow to configure, schedule, and monitor.
The flow is fully fault-tolerant: if streaming or loading fails, the entire process resumes from the last successful checkpoint once the issue is resolved.
Best for: low to moderate data volumes and teams prioritizing simplicity.
Consideration: Since streaming and loading are tightly coupled, a failure in one pauses both.

Option 2: Separate Extract and Load Flows

Use this option for maximum throughput, fault isolation, and horizontal scaling.

Build two independent flows:
- Extract Flow: streams CDC events into GS storage.
- Load Flow: loads staged data into BigQuery using native bulk operations.
The flows run in parallel and can be deployed across multiple nodes for large-scale workloads.
Best for: high-volume, low-latency pipelines and mission-critical systems where stability and performance are essential.

Prerequisites

1. Enable CDC in the source database:

2. The Google BigQuery instance must be available from the Internet.

3. The Google cloud storage account exists, and the user has read/write permissions for the specific bucket.

A pipeline with a single flow

BigQuery CDC flow streams CDC events into the designated Google storage bucket in real-time and periodically (as often as every second) loads the data into BigQuery in parallel with the stream.

Note: There is no need to create a separate Flow for the initial load. The first time it connects to a CDC-enabled source database, it reads a consistent snapshot of all of the included databases and tables. When that snapshot is complete, the Flow continuously reads the changes that were committed to the transaction log and generates the corresponding insert, update, and delete events.

Step 1. Create a CDC Connection for the source.

Read how to create a CDC connection.

Step 2. Create connection for staging files in Google Cloud storage

This loads staged files from Google Cloud storage. Read how to create GC storage connection.

Step 3. Create a BigQuery connection for the destination.

Step 4. Create a connection for history and offset files.

Read how to create CDC Offset and History connection.

Etlworks CDC connectors store the history of DDL changes for the monitored database in the history file and the current position in the transaction log in the offset file.

Typical CDC extract flow starts by snapshotting the monitored tables (A) or starts from the oldest known position in the transaction (redo) log (B), then proceeds to stream changes in the source database (C). If the Flow is stopped and restarted, it resumes from the last recorded position in the transaction log. The connection created in this step can be used to reset the CDC pipeline and restart the process from scratch.

The connection, by default, points to the directory{app.data}/debezium_data.

Step 5. Create BigQuery CDC flow.

In Flows clickAdd flow. Type incdc in Select Flow type. Select Stream CDC events into Google BigQuery.

Step 6. Add source-to-destination transformation

Left to right:

select the CDC connection
select tables to monitor in FROM
select the BigQuery connection
select or enter the BigQuery table name in TO. When streaming data from multiple source tables set the destination table using a wildcard template in the following format:project.schema.prefix_*_suffix, whereproject.schemais a BigQuery project and schema to load data into.

Step 7. Set connection for staging files in Google Cloud storage.

Select Connections tab, select connection created in step 2 and select CSV format.

Step 8. Configure load parameters

Click theMAPPINGbutton, select theParameterstab.

If needed modify the following Load parameters:

Load data into BigQuery every (ms): by default, the flow loads data into BigQuery every 5 minutes (300000 milliseconds). The load runs in parallel with the CDC stream, which never stops. Decrease this parameter to load data into BigQuery more often or increase it to reduce the number of consumed BigQuery credits.
Wait (ms) to let running load finish when CDC stream stops: By default, the flow loads data into BigQuery every 5 minutes. The CDC stream and load are running in parallel, so when streaming stops, the flow executes the load last more time to finish loading the remaining data in the queue. It is possible that the load flow is still running when the stream stops. Use this parameter to configure how long the flow should wait before executing the load last time. Clear this parameter to disable the wait. In this case, if the load task is still running, the flow will finish without executing the load one last time. The flow will load the remaining data in the queue on the next run.
Action: the action can beMERGE(default) orINSERT. If the action is set toMERGEthe flow will INSERT records that do not exist in the destination table, UPDATE existing records, and DELETE records that were deleted in the source table.
Lookup Fields:MERGEaction requires a list of columns that uniquely identify the record. By default, the flow will attempt to predict the Lookup Fields by checking unique indexes in the source and destination tables, but if there is no unique index in either table it is not guaranteed that the prediction will be 100% accurate. Use this parameter to define the Lookup Fields in the following format:fully.qualified.table1=field1,field2;fully.qualified.table2=field1,field2.

Note: The other parameters are similar or the same as for the flow type Bulk load files in Google Cloud Storage into BigQuery.

Step 9. Schedule BigQuery CDC flow.

We recommend using a continuous run Schedule type. The idea is that the Flow runs until it is stopped manually, there is an error, or (if configured) there are no more new CDC events for an extended period of time. It restarts automatically after a configurable number of seconds.

Monitor running CDC flow

Read how to monitor running CDC flow.

A pipeline with independent extract and load flows

CDC extract flow extracts data from a CDC-enabled database and creates CSV files with CDC events in the configured location. These files are loaded into the target database by Load Flow.

Step-by-Step Guide

Step 1. Create and schedule CDC Extract flow

This flow is used to extract CDC events from the source database in read time and create files with events.

Read how to create CDC extract flow.

Step 2. Create and schedule Bulk Load flow

This Flow is used to bulk load files created by the CDC extract flow into BigQuery.

Read how to create bulk load flow.

Step 3. Schedule Bulk Load Flow.

Schedule flow to run as often as needed. These are the options:

Run flow periodically (as often as once a minute)
Run flow continuously (as often as once a second).

Articles in this section

Create pipeline to CDC data into Google BigQuery