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How do you capture only changed data without putting strain on your source systems? This blog explains how change data capture works to keep analytics fast and up-to-date. It covers practical methods, tools, and use cases for building reliable real-time pipelines.
Businesses today need real-time analytics, not day-old snapshots. Pulling entire tables from a source database into warehouses or data lakes slows everything down. It creates inconvenient batch windows and delays decisions. This is where change data capture becomes useful.
But here’s the question. How do you capture only the changed data without overloading your source system?
This blog provides practical guidance on implementing data capture in production environments. It explains approaches, tools, and use cases that matter to teams who need fast, reliable pipelines.
Data pipelines used to depend on traditional batch processing. Large chunks of data were moved overnight. That process does not fit today’s requirements. Teams expect fresh data for dashboards, search indexes, and machine learning pipelines.
Change data capture helps by sending only changed data from the source system to target systems. Instead of copying existing data again, it streams updates continuously. This keeps target repositories up to date without extra load.
Key benefits include:
There are several methods for capturing changed data. Some methods are simple but heavy on performance. Others are lightweight but require more setup.
| Approach | Description | Pros | Cons |
|---|---|---|---|
| Trigger-based | Database triggers and trigger functions monitor data changes on source tables. | Easy to start, works with transactional databases and legacy databases. | Slows down transactions, tightly coupled with schema. |
| Shadow table | A separate table is used to store changed data over time. | Useful for auditing and delete operations. | Requires extra storage and maintenance. |
| Log based CDC | Reads from transaction log and transaction log files. | Minimal load, supports real time streaming and data replication. | Complex setup and parsing log file structure. |
| Stored procedures | Data changes are tracked during inserts or updates. | Flexible and application-driven. | Needs code updates and can add overhead. |
Log based cdc is often the most reliable for distributed system pipelines. It avoids hitting source tables and handles large data streams well.
Log based CDC reads directly from the transaction log. It avoids loading the same database tables again and again. This makes it lighter on the source system.
1-- Enable CDC in SQL Server 2EXEC sys.sp_cdc_enable_db; 3EXEC sys.sp_cdc_enable_table 4 @source_schema = N'dbo', 5 @source_name = N'orders', 6 @role_name = NULL; 7
In this example, CDC is enabled for the orders source table. A change table is created that records inserts, updates, and delete operations. These changes are then pushed to target systems such as warehouses, APIs, or search indexes.
This approach is reliable because CDC can replay transaction log files if a target repository fails. That means no lost events and consistent data replication across multiple systems.
The diagram below expands how CDC pipelines work from the source database to multiple target systems.
This flow shows how CDC captures changed data from the source database and streams it into warehouses, data lakes, search indexes, and dashboards.
CDC supports many practical use cases. It connects a source system with other systems while keeping them aligned with the same data.
Common scenarios include:
CDC is not limited to one sector. It is applied across industries to keep data consistent.
Examples include:
These examples show how cdc change data capture is applied in practical ways across multiple systems.
Running CDC in production involves more than setup. You must plan for scale, compliance, and data integration.
Important points include:
Real time analytics depends on quick access to the latest data. Traditional batch processing cannot meet this demand.
Change data capture allows only the data changes to move. This reduces payload size and speeds delivery. It also maintains data integrity across various systems, including warehouses and data lakes.
This approach helps teams run up to date dashboards, search indexes, and machine learning pipelines. It provides real time data without adding overhead on transactional databases.
With CDC in place, applications can receive the latest data streams in real time. Instead of spending weeks coding integrations, you can let Rocket.new do the heavy lifting. Build any app with simple prompts—no code required.
CDC pipelines require discipline and planning. Following best practices keeps them reliable in production.
Recommended practices include:
Change data capture is not just about tracking data changes. It is about making sure changed data flows correctly from the source system into target systems. Whether using database triggers, stored procedures, or log based cdc, the goal is the same. Deliver up to date data and maintain seamless data integration across multiple systems.
