Which of the Following Are the Type of Change Data Capturing in ETL System?


Heather Bennett

Which of the Following Are the Type of Change Data Capturing in ETL System?

Change data capturing (CDC) is a crucial component of an ETL (Extract, Transform, Load) system. It allows for the identification and extraction of changes made to source data, which can then be transformed and loaded into a Target system. By capturing only the changed data, CDC reduces processing time and improves efficiency in data integration processes.

The Types of Change Data Capturing in ETL Systems

There are several types of change data capturing techniques used in ETL systems:

1. Full Load

In this approach, all the source data is extracted and loaded into the Target system during each ETL process. While it ensures completeness, it can be time-consuming and resource-intensive.

2. Incremental Load

The incremental load method captures only the changes made since the last load. It compares a timestamp or a change sequence number to identify new or updated records. This technique significantly reduces processing time and resource usage.

3. Log-Based Capture

Log-based capture relies on database transaction logs to identify changes made to source data. It reads these logs and captures relevant information about inserts, updates, or deletes. This approach offers real-time or near-real-time capture capabilities but requires access to transaction logs.

4. Trigger-Based Capture

In trigger-based capture, triggers are placed on tables in the source database that fire whenever there is an insert, update, or delete operation. These triggers capture the necessary information about changed records and store them in dedicated tables for further processing by the ETL system.

5. Timestamp-Based Capture

Timestamp-based capture involves comparing the timestamps of source records with the timestamp of the last extraction. Any records with a more recent timestamp are considered changed and captured for processing. This technique is effective when the source system reliably updates timestamps upon record modifications.


In conclusion, change data capturing is a vital aspect of an ETL system as it allows for efficient and accurate updating of Target systems with only the necessary changes. The choice of CDC technique depends on various factors such as data volume, frequency of changes, and availability of transaction logs. By understanding these different types of change data capturing methods, you can select the most suitable approach for your ETL processes.

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