What is CDC Data Consistency?

The Problem CDC Solves: Modern applications rarely store everything in one database. You might have a PostgreSQL database for user transactions, an Elasticsearch cluster for search, Redis for caching, and Snowflake for analytics. Without CDC, teams often write to multiple systems simultaneously from application code (called dual writes). Dual writes create race conditions. Imagine your code writes an order to PostgreSQL successfully but crashes before updating Elasticsearch. Now your search index is missing that order. Or worse, the Elasticsearch write succeeds first, then PostgreSQL fails, and users see search results for orders that don't exist in your primary database. How CDC Provides Consistency: CDC treats your primary database as the single source of truth. It continuously reads the database's transaction log (the internal log where the database records every committed change) and streams those changes to downstream systems. This gives you three critical guarantees: First, transactional consistency: If your database commits three related changes in one transaction (like creating an order, decrementing inventory, and charging a payment), CDC ensures consumers see all three changes together, never partial results. Second, ordering guarantees: Changes to the same entity (like updates to user_id 12345) arrive in the exact order they were committed. Without this, a deletion could arrive before the creation, causing errors. Third, delivery semantics: CDC typically guarantees at least once delivery. Every change reaches consumers at least once, though you might see duplicates during retries. This prevents data loss but requires consumers to handle duplicates intelligently.