Identifying Use Cases for Change Data Capture

In this lesson, you will learn how to identify the specific scenarios where Change Data Capture (CDC) is the optimal strategy for maintaining real-time data synchronization without overwhelming your source databases.

When to Choose CDC

As a data engineer, the primary challenge in distributed systems is keeping downstream data stores (like search indexes, data warehouses, or cache layers) in sync with a transactional source of truth. You should implement CDC—reading transaction logs like MySQL’s binlog or PostgreSQL’s WAL—when traditional methods like query-based polling become too expensive.

Avoid query-based polling (e.g., SELECT * FROM orders WHERE updated_at > ?) when:

1. Source Impact Matters: Your source database is a high-traffic OLTP system. Running frequent, heavy polling queries causes table locks and CPU spikes that degrade performance for your application users.
2. Deletions Must Be Captured: A query-based approach cannot detect rows that were deleted. CDC captures the DELETE event directly from the log, ensuring your downstream system can drop records accordingly.
3. Latency Requirements are Strict: To achieve near-instantaneous synchronization across distributed services, CDC acts as a push-based mechanism. It streams every INSERT, UPDATE, and DELETE the moment they are committed to the transaction log.

Concrete Illustration

Imagine an e-commerce platform that needs to update a specialized search index every time a product price changes. Instead of forcing the search index to scan the products table every minute, you configure a Kafka Connect source connector to read the transaction logs:

{
  "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
  "database.hostname": "prod-db-server",
  "table.include.list": "public.products",
  "plugin.name": "pgoutput"
}

By offloading the synchronization task to the transaction log, your application remains decoupled from the search index infrastructure. The source database doesn't even "know" the search index is listening, effectively minimizing the performance footprint.

Use CDC when you need a "log-first" architecture that ensures data consistency without introducing the high overhead of periodic database scans.

Key Takeaway: Implement CDC when you need to capture hard deletes, reduce source database CPU contention, and minimize synchronization latency in a distributed architecture.