Change Data Capture (CDC) with Debezium on AWS

Introduction

Change Data Capture (CDC) is a crucial pattern in data engineering, capturing every change to a dataset or table and making it available for downstream systems. In this blog post, we’ll delve into implementing CDC using Debezium, an open-source CDC platform, on the AWS cloud.

What is CDC and Why Does It Matter?

Change Data Capture is essential for scenarios where:

• Auditing historical changes to data is necessary.
• Real-time data availability is crucial for analytical querying.
• Event-driven architecture requires services to operate in response to changes in data.

CDC: The E and L of Your Data Pipeline

In the context of data pipelines, CDC involves two main steps:

1. Capturing changes from the source system (E):

• Utilizing the transaction log of the database to extract changes.
• Incremental extraction using ordered columns.
• Snapshot extraction for entire data.

2. Making changes available to consumers (L):

• Extracting and loading change data into a shared location like Amazon S3.
• Directly loading change data into a destination system.

Project Overview

Objective: Capture every change in a MySQL database and make it available for analytics.

Components of the Data Pipeline:

• Upstream: MySQL database with user and product tables.
• Kafka Connect Cluster: Using Debezium connector to extract data from MySQL and load it into Kafka.
• Kafka Cluster: Making change data available for downstream consumers.
• Data Storage: Leveraging Minio (Amazon S3 alternative) to store data generated by Debezium.
• Data Warehouse: Utilizing duckDB to ingest data from Amazon S3 and create an SCD2 table.

AWS Environment Setup

To implement this project on AWS, consider the following AWS services:

• AWS Cloud9: Cloud-based integrated development environment for collaborative coding.
• Amazon S3: For scalable and secure storage of change data.
• Amazon Kafka on MSK (Managed Streaming for Kafka): Fully managed Kafka service for building real-time data streaming applications.

Implementation Steps

1. Environment Setup:

Set up AWS Cloud9 for a collaborative coding environment.

Ensure Docker is installed for containerized development.

2. Debezium Configuration:

Adjust Debezium connector configurations for MySQL and Kafka.

Start Docker containers for Kafka, Zookeeper, and MySQL.

3. Change Data Extraction:

Use Debezium to capture changes from MySQL and push them to Kafka topics.

4. Data Loading into Amazon S3:

Set up connectors to extract data from Kafka and load it into an Amazon S3 bucket.

5. Analysis with duckDB:

Write queries in duckDB to analyze change data and create an SCD2 dataset.

Caveats and Best Practices

• Handling Bulk Changes: Ensure scalability of Kafka and Kafka Connect clusters for backfills or bulk changes.
• Schema Changes: Implement mechanisms to handle schema changes gracefully.
• Incremental Key Changes: Carefully manage incremental key changes to avoid data inconsistencies.

Conclusion

In the ever-evolving landscape of data engineering, embracing tools like Debezium on AWS positions you at the forefront of scalable and efficient data processing.

Drop a query if you have any questions regarding CDC or Debezium and we will get back to you quickly.

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