Change Data Capture
Reading data from databases as event-streams
Tools for CDC
Feel free to read more about some of the common tools used to implement CDC
Here is the consolidated, comprehensive article. I have merged the conceptual framework of the second article (strategies and patterns) with the technical depth and ecosystem overview of the first article.
I have also organized it logically: defining the problem → explaining the solutions → detailing the modern standard (Log-based) → listing use cases and tools.
Change Data Capture: The Definitive Guide
Data rarely stays static. In modern software architecture, the core challenge is not just storing data, but moving it. You might have a primary transactional database (like PostgreSQL or MySQL), but you also need that same data in other systems:
Cloud Data Warehouses (Snowflake, BigQuery) for analytics.
Search Indexes (Elasticsearch, Algolia) for full-text search.
Microservices that need synchronized views of data.
Caches (Redis) to improve read performance.
The process of identifying and moving these changes is known as Change Data Capture (CDC).
The Strategy: Full Load vs. Incremental
When syncing data between a source (your database) and a target (your warehouse or app), there are two fundamental strategies:
1. Full Snapshots (The "Naive" Approach)
In this approach, you periodically extract the entire dataset from the source and replace the data in the target system.
Pros: Straightforward implementation; ensures consistency.
Cons: As data volume grows, copying and re-indexing the entire database becomes slow and resource-intensive. It is often impractical for real-time needs because the data is outdated the moment the snapshot finishes.
2. Incremental Load (CDC)
Instead of moving the whole mountain, you only move the rocks that shifted. CDC identifies and extracts only the data that has changed (Inserts, Updates, Deletes) since the last sync.
Pros: Highly efficient, reduces network load, and enables near real-time data synchronization.
Cons: Requires more complex logic to implement reliably
Implementation Patterns: How CDC Works
Two approaches to CDC
Push: This approach requires you to implement some sort of logic or process to capture changes in the source database. Then it relies on the source database to push any data updates to the target system when something changes in the source system. This method allows the target systems to be updated with the latest data in near real-time, but if you don't set this up properly, you risk losing data updates if the target systems are unreachable when the source systems try to push the changes.
Pull: This approach requires the target systems to continuously poll the source database to check for changes and then pull in data updates when they happen. This method typically results in a lag before the target systems pull in any new data updates because the changes are usually batched between pull requests.
Not all CDC is created equal. There are three primary patterns for implementing CDC, ranging from simple polling to complex log analysis.
1. Batch-oriented or query-based CDC (Pull)
This method relies on querying the database to find changes. It typically requires an "audit column" in your table, such as last_updated_at or modification_timestamp.
Mechanism: The target system periodically queries the source:
SELECT * FROM table WHERE last_updated > [Last_Sync_Time].Pros: Easy to implement using standard SQL; works with almost any database.
Cons:
Performance Overhead: Constant polling puts load on your primary database.
Missed Deletes: Standard SQL queries cannot detect when a row is deleted (unless you use "soft deletes").
Latency: There is always a lag between the event and the next poll.
2. Trigger-Based CDC (Push)
Database triggers are stored procedures that automatically run when a specific event occurs.
Mechanism: You create a trigger that fires on
INSERT,UPDATE, orDELETE, copying the changed data into a separate "shadow table" or pushing it to an external system.Pros: Captures deletes and happens immediately.
Cons: Triggers drastically reduce write performance on the database. They are widely considered tech debt in high-throughput systems.
3. Continuous or log-based CDC (The Modern Standard, Pull)
This is the most robust approach used by modern data architectures.
Mechanism: Every database writes to a transaction log (e.g., MySQL
binlog, PostgreSQL Write-Ahead Log, MongoDBoplog) for crash recovery. Log-based CDC connects to this log and streams the events.Pros:
Zero Impact: It reads the log files, not the data tables, so it doesn't slow down database queries.
Completeness: Captures all events, including deletes.
Real-Time: Changes are streamed instantly.
Deep Dive: The Architecture of Log-Based CDC
Log-based CDC essentially treats your external systems as "followers" of your database. Just as a database replica stays in sync by reading the leader's logs, CDC tools read those logs to update search indexes, caches, or data lakes.
To implement this successfully, a robust CDC pipeline (like Debezium) usually performs two distinct operations:
The Snapshot: It takes a consistent point-in-time snapshot of the existing data.
The Stream: It switches to reading the transaction log to capture ongoing changes.
Note: While the stream contains the history, databases eventually purge old logs to save space. Therefore, you almost always need the initial snapshot to bootstrap the process.
Key Use Cases
CDC bridges the gap between "Data at Rest" and "Data in Motion," enabling several critical patterns:
Real-Time Analytics: Streaming changes directly to data warehouses or lakehouses (Databricks, Delta Lake) for up-to-the-second dashboards.
Event-Driven Microservices: Implementing the "Outbox Pattern." Instead of a service calling another service synchronously (which is brittle), it writes to its own DB, and CDC picks up that change to notify other services.
Search & Cache Sync: Keeping Elasticsearch or Redis automatically synchronized with the primary database without writing custom dual-write code in the application layer.
Audit and Compliance: Creating an immutable audit trail of exactly what changed, who changed it, and when—vital for GDPR and financial regulations.
The CDC Ecosystem: Tools and Platforms
The landscape for CDC has matured significantly. Here are the main categories of tools available today:
1. Open Source (Self-Hosted)
Debezium: The de facto standard for open-source CDC. It runs on top of Apache Kafka and provides connectors for MySQL, PostgreSQL, MongoDB, SQL Server, and Oracle. It offers low latency and guarantees at-least-once delivery.
2. Cloud-Native Managed Services
AWS Database Migration Service (DMS): Handles ongoing replication between heterogeneous databases.
Google Cloud Datastream: Serverless CDC native to the GCP ecosystem.
Azure Data Factory: Provides CDC capabilities within the Azure suite.
3. Managed CDC Platforms
Fivetran & Airbyte: Focus on "ELT" (Extract, Load, Transform). They abstract away the complexity of logs and provide simple pipelines to move data from DBs to Warehouses.
Striim: An enterprise-grade platform for real-time data integration and stream processing.
Modern Considerations
Schema Evolution: Modern CDC tools handle schema changes gracefully, propagating DDL changes and managing backward/forward compatibility.
Exactly-Once Semantics: With technologies like Kafka's transactional APIs and idempotent writes, many CDC pipelines now achieve exactly-once processing guarantees.
Multi-Cloud and Hybrid: CDC solutions increasingly support streaming data across cloud providers and between on-premises and cloud environments.
Security and Compliance: Modern CDC implementations include encryption in transit and at rest, GDPR-compliant data masking, and fine-grained access controls.
The beauty of CDC is that it bridges two worlds: it lets you maintain your existing database-centric applications while unlocking the benefits of event-driven, log-based architectures for data integration and processing. What started as a niche technique has become a fundamental building block of modern data architectures, enabling organizations to build responsive, scalable, and maintainable data systems.
Summary
What started as a niche technique for database replication has become a fundamental building block of modern data engineering. By moving from "pulling snapshots" to "streaming logs," CDC allows organizations to build responsive, scalable systems where data flows freely between applications and analytics environments.
CDC Visual Demo
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