This is an introduction to Change Data Capture
What is it?
Change Data Capture (CDC) is a set of techniques or tools used to identify and capture changes made to data in a database or data source. This is often used in data integration, data warehousing, and real-time analytics to ensure that changes in the source system are reflected in the target system or application.
How works?
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Monitoring: CDC involves monitoring the data source for changes. This can be done in various ways depending on the database system and the CDC method used.
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Capture: Once changes are detected, CDC captures these changes. This can include new records, updates to existing records, and deletions.
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Transformation: Sometimes, captured changes need to be transformed or formatted to match the requirements of the target system.
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Loading: The transformed changes are then loaded into the target system, which could be another database, a data warehouse, or an analytics platform.
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Application: The target system applies these changes to keep its data synchronized with the source.
Benefits
Real-Time Data Synchronization: Keeps the target systems up-to-date with minimal delay, which is critical for real-time analytics and decision-making.
Efficient Data Integration: Reduces the need for full data extracts and transfers, which can be resource-intensive.
Improved Data Accuracy: Ensures that changes are consistently and accurately reflected across systems.
Reduced Latency: Helps in minimizing the lag between when a change occurs and when it is reflected in the target system.
Use Cases
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Data Warehousing: To keep the data warehouse synchronized with operational databases.
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Real-Time Analytics: To ensure analytics platforms reflect the latest data.
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ETL Processes: For efficient Extract, Transform, Load (ETL) processes by capturing only changed data.
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Data Replication: To replicate changes from one database to another.
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Microservices: CDC is well known patern in Microservices Oriented Architectures
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Getting Started: Get started with Idra