What is Change Data Capture?
Change Data Capture (CDC) is a design pattern used in databases and data processing systems. It captures changes made at the data source, minimizes the resources required for ETL (extract, transform, load) processes, and maintains the validity and timeliness of the data.
History
Change Data Capture has its roots from the early days of database technology when maintaining synchrony between databases was a daunting task. Over time, as database systems evolved, the need for efficient and reliable data update methods became more apparent, paving the way for CDC.
Functionality and Features
CDC tracks and logs data changes in a database including insertions, updates, and deletions. This log can then be used by a separate process to update data in a target data warehouse, ensuring real-time or near real-time data integration. CDC reduces the need for batch processing during off-peak hours and enables smoother and more continuous data processing.
Architecture
In CDC, source databases have a CDC instance that captures and stores changes. These changes are then propagated to target databases or applications which have their own CDC instances for receiving and processing the data updates. This chain provides continuous data integration and a consistent view across all systems.
Benefits and Use Cases
CDC reduces the strain on operational systems and enhances data timeliness, making it ideal for real-time analytics, business intelligence, and data warehousing. In addition, the data captured is easier to manage and can be used to derive valuable, actionable insights.
Challenges and Limitations
While CDC greatly improves data processing and analysis, it can be complex to implement. Also, it might not capture all changes in high-transaction environments, missing some data updates. Regular checks are required to ensure consistency.
Integration with Data Lakehouse
In a data lakehouse environment, CDC can be a critical component. It ensures data in the lakehouse is current and accurate, which is essential for real-time analytics and business intelligence. With CDC, data lakehouse users can work with real-time or near real-time data.
Security Aspects
CDC operates on data at a fundamental level, making security critical. Encryption, data masking, and strict access controls are typically employed to ensure data safety. Data integrity checks are also crucial to prevent any data tampering.
Performance
By minimizing the resources required for ETL processes and reducing the need for batch processing, CDC optimizes performance, making data readily available for analytics and business intelligence.
FAQs
What kind of data changes does CDC capture? CDC captures all types of data changes including insertions, updates, and deletions.
Is it necessary to have CDC in a data lakehouse environment? While not necessary, CDC does enhance data timeliness and validity in a data lakehouse environment.
What are the challenges in implementing CDC? CDC can be complex to implement and might not capture all changes in high-transaction environments.
Glossary
ETL: Extract, transform, load - a data integration process.
Data Lakehouse: A hybrid data management platform combining the features of data warehouses and data lakes.
Real-Time Analytics: The use of, or the ability to use, data and related resources as soon as the data enters the system.
Business Intelligence: Technology-driven processes for analyzing data and presenting actionable information.
Data Tampering: Unauthorized changes to digital data.
