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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 the relational database which supports the bulk load.

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 Vertica database in real time.

Flows optimized for Vertica

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

How it works

The end-to-end CDC pipeline extracts data from a CDC-enabled database and loads it into the Vertica.

There are three options when creating a pipeline:

Pipeline	Description	Supported operations
Generic CDC to relational database flow	This is a standard "CDC to any database" flow that supports any relational database as a destination, including Vertica. Unlike some other analytical databases, such as Snowflake and Redshift, etc. Vertica supports fast INSERT without using the bulk-load COPY command.	INSERT
A flow optimized for streaming CDC events directly into Vertica	A single flow streams CDC events into local or cloud storage and periodically loads them into target tables using native Vertica 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.	INSERT and MERGE
A pipeline with independent 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 or cloud storage. Load Flow: loads staged data into Vertica 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.	INSERT and MERGE

Prerequisites

1. Enable CDC in the source database:

2. Vertica database is up and running and accessible from the Etlworks instance. Read how to work with on-premise data in Etlworks.

A pipeline with a single flow

Create and schedule CDC flow with bulk load

CDC flow streams CDC events into the designated stage in the local or cloud storage in real-time and periodically (as often as every second) loads the data into the Vertica database 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 (optional). Create connection for staging files in a cloud storage

If you are planning to use cloud storage such as AWS S3 or Google Cloud Storage for staging and loading file create one of the following connections:

Amazon S3 - for staging files in S3.
Google Cloud Storage - for staging files in GC storage.

This step is not required if the flow loads locally staged files.

Step 3. Create a connection for the Vertica database.

Read about Vertica connector. It is recommended to enable the auto-commit for the destination connection.

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 CDC flow.

In Flows click Add flow. Type incdc in Select Flow type. Select Stream CDC events into Vertica.

Step 6. Add source-to-destination transformation

Left to right:

select the CDC connection
select tables to monitor in FROM
select the database connection
select or enter the destination table name in TO. When streaming data from multiple source tables set the destination table using a wildcard template.

Step 7 (optional). Set connection for staging files in cloud storage.

If you are planning to stage files in AWS S3 bucket or Google Cloud Storage select Connections tab, select connection created in step 2 and select CSV format.

Read how to load files from S3 bucket.
Read hot to load files from GC storage.

Step 8. Configure load parameters

Click the MAPPING button, select the Parameters tab.

If needed modify the following Load parameters:

Load data into Vertica every (ms): by default, the flow loads data into Vertica every 5 minutes (300000 milliseconds). The load runs in parallel with the CDC stream, which never stops. Decrease this parameter to load data into Vertica more often or increase it to reduce the number of consumed database credits.
Wait (ms) to let running load finish when CDC stream stops: By default, the flow loads data into Vertica 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.
Bulk Load SQL: the Bulk Load SQL is used to load files in the cloud or file storage into the staging tables in Vertica database. This is a required parameter.
By default, we assume that the flow bulk-loads from the local (server) storage usingCOPY FROM LOCALcommand.
```
COPY {TABLE} FROM LOCAL '{FULL_FILE_NAME}' 
PARSER fcsvparser(header='true') ABORT ON ERROR;
```
You can modify the SQL statement that will be used to bulk-load data into the destination table. The following {tokens}can be used as a part of the SQL statement:
- {TABLE}: the table to load data into.
- {TEMP_TABLE}: the staging table name used for MERGE.
- {FILE_TO_LOAD}: the file name to load without path and extension.
- PATH: the path of the file to load without file name, for example{app.data}/test/.
- EXT: the file extension of the file to load, without., for example,csv.
Read about VerticaCOPYcommand options.
Action: the action can be MERGE (default) or INSERT. If the action is set to MERGE the 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 Vertica.

Step 9. Schedule 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 Vertica.

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 Vertica