Real-Time Data Replication Using Change Data Capture (CDC) with Debezium and Kafka

In today’s world of distributed systems and real-time analytics, keeping data in sync across databases is critical. This blog post walks you through a practical demo of implementing Change Data Capture (CDC) to replicate data from SQL Server to PostgreSQL, using Debezium, Apache Kafka, and Kafka Connect.

We’ll set up a fully functional pipeline that captures changes in SQL Server and streams them to a PostgreSQL sink — all within Docker containers.

📌 What is Change Data Capture (CDC)?

Change Data Capture (CDC) is a technique for identifying and capturing changes made to data in a database. Instead of periodically polling for changes, CDC enables near real-time change propagation by reading the database’s transaction log.

  • Real-time data sync between systems
  • Reduced overhead compared to full data refreshes
  • Event-driven architectures support
  • Seamless integration with analytics and microservices platforms

🏗️ System Architecture

Here’s the overall architecture of our CDC pipeline:

CDC Architecture Diagram

  • SQL Server with CDC enabled: Source system where changes originate.
  • Debezium SQL Server Connector: Reads the SQL Server transaction log.
  • Kafka: Acts as the streaming backbone.
  • Kafka Connect: Handles integration with source and sink systems.
  • PostgreSQL: The destination system (sink).

🧰 Prerequisites

  • Docker & Docker Compose
  • curl (for interacting with Kafka Connect)
  • Basic knowledge of SQL and Docker

🚀 Step-by-Step Setup

📂 Project Files

All configuration files are available in the kamranzafar/cdc-demo GitHub repository. Clone this repository on your local machine. This repository contains the following source files, which are required by this setup.

1️⃣ Start the Environment

Using command line, run the following command to start the services:

docker-compose -f docker/docker-compose.yml up

2️⃣ Configure SQL Server

✅ Enable SQL Server Agent

Log inside the SQL Server docker container and run the following command.

/opt/mssql/bin/mssql-conf set sqlagent.enabled true

Restart the container after enabling the agent.

🧱 Create and Enable CDC on Source Table


CREATE DATABASE TESTDB;
USE TESTDB;

CREATE TABLE messages (
  id INT,
  msg VARCHAR(255)
);

EXEC sys.sp_cdc_enable_db;

EXEC sys.sp_cdc_enable_table
  @source_schema = N'dbo',
  @source_name   = N'messages',
  @role_name     = NULL;

3️⃣ Setup PostgreSQL Sink Table


CREATE TABLE messages (
  "id" INT8 NOT NULL,
  "msg" VARCHAR(255),
  PRIMARY KEY ("id")
);

4️⃣ Configure Kafka Connect Connectors

Run the following to deploy the source and sink connectors:


curl -i -X POST -H "Accept:application/json" \
  -H "Content-Type:application/json" \
  localhost:8083/connectors -d @source-connector.json

curl -i -X POST -H "Accept:application/json" \
  -H "Content-Type:application/json" \
  localhost:8083/connectors -d @sink-connector.json

🧪 Testing the Pipeline

➕ Insert Data into SQL Server


INSERT INTO dbo.messages VALUES (1, 'message 1');
INSERT INTO dbo.messages VALUES (2, 'hello world');

🔍 Query Data in PostgreSQL

SELECT * FROM messages;

You should see the replicated rows appear!

🧩 Summary

In this demo we set up a real-time CDC pipeline using:

  • SQL Server with CDC enabled
  • Debezium connector
  • Apache Kafka
  • Kafka Connect
  • PostgreSQL sink

This architecture is ideal for syncing data between systems, powering real-time dashboards, or feeding data lakes.

📎 Next Steps

  • Support updates and deletes
  • Handle schema evolution
  • Secure your connectors and broker
  • Monitor pipeline health with tools like Kafka UI or Prometheus

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