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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 Greenplum.

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 the Greenplum database in [almost] real-time.

Flows optimized for Greenplum

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

How it works

A CDC pipeline into Greenplum captures real-time changes from a CDC-enabled source database, stages them in local storage and loads them using Greenplum’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 local storage and periodically loads them into target tables using native Greenplum 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 local storage.
- Load Flow: loads staged data into Greenplum 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 Greenplum instance must be available from the Internet.

3. The gpload utility must be installed on the same VM as Etlworks. Contact Etlworks support atsupport@etlworks.comif you need assistance installing the gpload.

Install and configure Greenplum gpload utility

Read how to install and configure the Greenplum gpload utility and the command to execute the gpload.

A pipeline with a single flow

Greenplum CDC flow streams CDC events into the designated server storage location in real-time and periodically (as often as every second) loads the data into Greenplum 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 database.

Read how to create a CDC connection.

When creating a CDC connection, enable Do not enclose null values in double quotes.

Step 2. Optionally configure storage location.

Read how to change the default location.

Step 3. Create a Greenplum 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 Greenplum CDC flow.

In Flows clickAdd flow. Type incdc in Select Flow type. SelectStream CDC events into Greenplum.

Step 6. Add source-to-destination transformation

Left to right:

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

Step 6. Configure load parameters

Click theMAPPINGbutton, select theParameterstab.

If needed modify the following Load parameters:

Load data into Greenplum every (ms): by default, the flow loads data into Greenplum every 5 minutes (300000 milliseconds). The load runs in parallel with the CDC stream, which never stops. Decrease this parameter to load data into Greenplum more often.
Wait (ms) to let running load finish when CDC stream stops: By default, the flow loads data into Greenplum 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 into Greenplum.

Step 7. Schedule Greenplum 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 Greenplum.

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 Greenplum