Change Data Capture: Real-Time Sync Patterns and Tools
Try This First
Test your knowledge before reading. Don't worry if you get it wrong — that's part of learning.
Key Insights
- Change Data Capture (CDC) captures row-level changes in databases and streams them to downstream systems in real time.
- This module covers log-based CDC (Debezium, AWS DMS), query-based CDC, and trigger-based approaches, practical deployment with Kafka Connect and Pulsar, and 2025-2026 trends including peerDB for Postgres CDC, Snowpipe Streaming, and the convergence of CDC with real-time analytics architectures.
Overview
Change Data Capture (CDC) is a pattern for capturing changes made to a database and applying them to downstream systems in real time. Instead of running periodic batch loads, CDC captures inserts, updates, and deletes as they happen and streams them to targets. CDC is fundamental to modern data architectures that require low-latency synchronization between operational and analytical systems.
There are several CDC implementation approaches: log-based CDC reads the database transaction log to capture changes without impacting source performance; trigger-based CDC uses database triggers to record changes; and query-based CDC compares snapshots to detect differences. Log-based CDC is generally preferred for production systems due to its minimal overhead and completeness.
Key Concepts
- Log-Based CDC: Captures changes directly from the database transaction log without modifying the source application or schema.
- Trigger-Based CDC: Uses database triggers to capture changes into a separate tracking table, offering flexibility but adding overhead.
- Debezium: An open-source distributed CDC platform built on Apache Kafka Connect, supporting MySQL, PostgreSQL, MongoDB, and more.
- Kafka Connect: A framework for streaming data between Apache Kafka and external systems with built-in CDC source connectors.
- Exactly-Once Semantics: The guarantee that each change is processed exactly once, preventing both duplicates and data loss.
Key Takeaways
- CDC captures database changes in real time and streams them to downstream systems without batch loads.
- Log-based CDC reads transaction logs with minimal performance impact and complete change coverage.
- Debezium and Kafka Connect are the dominant open-source CDC tooling ecosystem.
- Exactly-once semantics prevent data loss and duplication in CDC pipelines.
Article Metadata
Review with Spaced Repetition
Add this lesson's 4 flashcards to your SM-2 study queue. They will appear when due in the Study Queue.
Feynman Concept Cards
Master each building block: read the ELI5, explore the analogy, work the example, find your gaps, teach it back, build it.
Change Data Capture is a concept in advanced techniques. In simple terms, Change Data Capture covers advanced techniques in Data Engineering. This data engineering concept addresses key topics in the advanced techniques in data engineering domain. Also known as: CDC, change
Analogy
Example
Find Gaps
Explain Change Data Capture as if teaching a colleague who is new to advanced techniques. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Change Data Capture in a real-world advanced techniques scenario. Walk through your design decisions.
Show solution
A diagram for Change Data Capture should include: 1. The core components of cdc 2. How they interact 3. Expected outcomes or outputs
Apache Kafka is a concept in advanced techniques. In simple terms, Apache Kafka covers advanced techniques in Data Engineering. This data engineering concept addresses key topics in the advanced techniques in data engineering domain. Also known as: Kafka. Related con
Analogy
Example
Find Gaps
Explain Apache Kafka as if teaching a colleague who is new to advanced techniques. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Apache Kafka in a real-world advanced techniques scenario. Walk through your design decisions.
Show solution
A diagram for Apache Kafka should include: 1. The core components of apache kafka 2. How they interact 3. Expected outcomes or outputs
Change Data Capture Patterns is a concept in streaming. In simple terms, Change Data Capture Patterns covers streaming data architecture in Data Engineering. This data engineering concept addresses key topics in the streaming data architecture in data engineering domain. A
Analogy
Example
Find Gaps
Explain Change Data Capture Patterns as if teaching a colleague who is new to streaming. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Change Data Capture Patterns in a real-world streaming scenario. Walk through your design decisions.
Show solution
A diagram for Change Data Capture Patterns should include: 1. The core components of change data capture 2. How they interact 3. Expected outcomes or outputs
Feynman Synthesis — Prove You Understand
1. The One-Pager
Explain this lesson's core idea to a smart 15-year-old. No jargon allowed.
2. The Gap Map
List 3 things you are still unsure about. Be specific.
Knowledge Check
Test your understanding of this lesson.
Flashcards
Space = flip · 1-4 = grade · Swipe on mobile