Realtime Change Data Capture Streaming | End to End Data Engineering Project

Introduction

This tutorial guides you through implementing Change Data Capture (CDC) for real-time data streaming using a tech stack that includes Docker, Postgres, Debezium, Kafka, Apache Spark, and Slack. By following these steps, you'll create an efficient data pipeline that can handle live data changes effectively. This process is especially relevant for data engineers looking to enhance their skills in real-time data processing and streaming.

Step 1: Understand the System Architecture

Before diving into implementation, familiarize yourself with the architecture.

• Components:

  • Postgres: The database where your data resides.
  • Debezium: A CDC tool that captures changes in the database.
  • Kafka: A distributed event streaming platform that manages data streams.
  • Apache Spark: A powerful analytics engine for big data processing.
  • Docker: Used to containerize applications for easy deployment.
  • Slack: For notifications or alerts.

• Flow:

  1. Changes in the Postgres database are captured by Debezium.
  2. Debezium publishes these changes to Kafka.
  3. Apache Spark processes the data as it streams through Kafka.

Step 2: Set Up Your Environment

To start building your CDC pipeline, you need to set up your environment.

• Install Docker:

  • Follow the Docker installation guide for your operating system.

• Run Postgres in Docker:

  docker run --name postgres -e POSTGRES_PASSWORD=mysecretpassword -d -p 5432:5432 postgres
  

• Access Postgres:

  • Use a Postgres client or command line to connect to your database.

Step 3: Load Live Data into Postgres

You need to populate your Postgres database with live data for CDC.

• Create a sample table:

  CREATE TABLE users (
      id SERIAL PRIMARY KEY,
      name VARCHAR(100),
      email VARCHAR(100)
  );
  

• Insert sample data:

  INSERT INTO users (name, email) VALUES ('John Doe', 'john@example.com');
  

Step 4: Connect Debezium and Kafka

Next, connect Debezium to your Postgres database and Kafka.

• Download Debezium:

  • Use Docker to run Debezium:

  docker run -it --rm \
      --name debezium \
      -e GROUP_ID=1 \
      -e CONFIG_FILE=debezium-connector-postgres.json \
      -p 8083:8083 \
      debezium/connect
  

• Configure the Debezium connector:

  • Create a JSON configuration file (debezium-connector-postgres.json):

  {
      "name": "postgres-connector",
      "config": {
          "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
          "tasks.max": "1",
          "database.hostname": "postgres",
          "database.port": "5432",
          "database.user": "postgres",
          "database.password": "mysecretpassword",
          "database.dbname": "yourdbname",
          "database.server.name": "dbserver1",
          "table.whitelist": "public.users",
          "snapshot.mode": "schema_only"
      }
  }
  

• Post the configuration to Debezium:

  curl -X POST -H "Content-Type: application/json" --data @debezium-connector-postgres.json http://localhost:8083/connectors
  

Step 5: Preview Data on Kafka

Now, confirm that Debezium is successfully capturing data changes by previewing the Kafka topics.

• List Kafka topics:

  kafka-topics --list --bootstrap-server localhost:9092
  

• Consume data from a Kafka topic:

  kafka-console-consumer --bootstrap-server localhost:9092 --topic dbserver1.public.users --from-beginning
  

Step 6: Handling Changes in Data

Learn how to handle specific data changes such as decimal values and user information.

• Configure Debezium settings to handle decimal values.
• Create triggers in Postgres to capture user changes.

Step 7: Enhance the Data Capture Process

Improve the robustness of your CDC pipeline.

• Implement data validation to ensure data integrity.
• Use Apache Spark to process and analyze streaming data from Kafka.

Conclusion

In this tutorial, you learned how to set up a Change Data Capture pipeline using Postgres, Debezium, Kafka, and Apache Spark. You now have the foundational skills to build a real-time data streaming application. For further learning, consider exploring the full code available on GitHub and the accompanying Medium article for deeper insights into each component. Happy coding!