CDC involves capturing and storing only the data changes that occur in the source system, which can reduce the amount of data that needs to be loaded into the target system. If the target system has limitations on the amount of data it can store or the rate at which data can be loaded, CDC may be a better choice. CDC involves capturing and storing data changes as they occur in the source system, which can be useful if the source system has specific data tracking or auditability requirements. If the source system has specific requirements for data tracking or auditability, CDC may be the better choice. CDC involves capturing and storing data changes as they occur in the source system, which can be used to maintain a record of data changes over time. If you need to track the history of data changes over time and maintain an auditable record of those changes, CDC may be a better choice. If you need to keep the data in the target system in sync with the data in the source system in real-time, CDC may be the right approach. Real-time data synchronization:ĬDC involves continuously monitoring the source system for changes to data and capturing and storing those changes in a separate log or repository. There are several scenarios where you might choose to use CDC (Change Data Capture) over ETL (Extract, Transform, Load) to move and process data from one system to another: 1. Data engineers need to understand the differences between these approaches and choose the appropriate one based on the specific needs and requirements of the data pipeline they are building. ETL is often used to consolidate data from multiple sources into a single repository for analysis and reporting.īoth CDC and ETL are commonly used in data engineering to move and process data from one system to another, but they serve different purposes and are used in different contexts. The transformed data is then loaded into the target system, typically a data warehouse or data lake. ![]() ETL involves extracting data from the source system, cleaning and preprocessing the data to remove errors or inconsistencies, and transforming the data into a format that is suitable for the target system. CDC is often used to keep the data in a target system in sync with the data in the source system or to track changes to data over time.ĮTL (Extract, Transform, Load) refers to a set of processes that involve extracting data from one or more sources, transforming the data to meet the needs of the target system, and loading the data into the target system. These changes are then captured and stored in a separate log or repository, which can be used to update a target system. ![]() CDC (Change Data Capture) and ETL (Extract, Transform, Load) are two different approaches to moving and processing data from one system to another.CDC refers to the process of capturing and tracking changes to data as they occur in a source system.ĬDC involves continuously monitoring the source system for changes, such as new records being added, existing records being updated, or records being deleted.
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