Change Data Capture (CDC) Tutorial: Get Started in Minutes
Streamkap Team
December 15, 2024
TL;DR
CDC captures database changes in real-time, enabling low-latency data synchronization without impacting source database performance.
Table of Contents
What is Change Data Capture? Why Use CDC? How Streamkap Implements CDC Getting StartedWhat is Change Data Capture?
Change Data Capture (CDC) is a set of software design patterns used to determine and track data that has changed in a database. Instead of periodically querying for all data, CDC captures only the changes (inserts, updates, deletes) as they happen.
Why Use CDC?
Traditional batch ETL processes have several limitations:
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High latency - Data can be hours or days old
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Database load - Full table scans put strain on source databases
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Missed changes - Updates between batch runs can be lost
CDC solves these problems by:
1. Capturing changes in real-time as they occur
Minimizing database impact by reading transaction logs 3.
Ensuring data completeness with ordered, exactly-once delivery
How Streamkap Implements CDC
Streamkap uses Debezium connectors to read database transaction logs (WAL for PostgreSQL, binlog for MySQL) and stream changes to your destinations in real-time.
Supported Sources
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PostgreSQL
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MySQL
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MongoDB
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SQL Server
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Oracle
Supported Destinations
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Snowflake
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Databricks
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ClickHouse
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BigQuery
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Kafka
Getting Started
1. Sign up for a free Streamkap account
2. Connect your source database
3. Configure your destination data warehouse
4. Start streaming in minutes
Ready to get started? Sign up for free and start syncing your data in real-time.
Streamkap Team
LinkedInAuthor Bio
The Streamkap team builds modern real-time data infrastructure to help companies sync data between systems.
Published
December 15, 2024
TL;DR
CDC captures database changes in real-time, enabling low-latency data synchronization without impacting source database performance.
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Change Data Capture for Streaming ETL
Change Data Capture refers to the process of capturing changes made to data in a source system such as a database, so that these change events can be used in the destination system, such as a data warehouse, data lake, data app, machine learning models, indexes, or caches.