Change data capture (CDC) in a nutshell

Change data capture (CDC) is a technique used to capture changes made to data in real-time or near-real-time and replicate those changes to other systems or data warehouses. The primary goal of CDC is to provide accurate and up-to-date data for use in various business applications and analytics.

CDC captures changes to data by monitoring the data source's transaction log or database journal, which contains a record of every change made to the data. The CDC system reads these changes and updates the target system accordingly. This process allows for real-time data replication, which can be critical for applications that require up-to-date information, such as financial systems, healthcare systems, and logistics systems.

Advantages

Real-Time

One of the advantages of CDC over traditional ETL (extract, transform, load) methods is that it can capture and replicate changes in real-time or near-real-time. In contrast, traditional ETL processes typically run on a scheduled basis, such as nightly or weekly, which means that the data in the target system may not be up-to-date. Additionally, CDC can reduce the amount of data that needs to be processed compared to traditional ETL because it only captures changes made to the data rather than processing all of the data every time.

Reduce processing time

CDC also reduces data processing time because it only captures changes made to the data rather than processing all of the data every time. This means that organizations can process data more efficiently, which can be particularly useful when dealing with large datasets.

Replication

Another advantage of CDC is that it can be used to replicate data between different types of databases, such as from a relational database to a NoSQL database, or from an on-premise database to a cloud-based database. This flexibility allows organizations to use the best tools for their specific use case without being limited by data format or location.

Summary

Overall, CDC is a valuable tool for organizations that need to maintain accurate and up-to-date data for use in various business applications and analytics. Its ability to capture and replicate changes in real-time or near-real-time, reduce data processing time, and provide flexibility in data replication make it a powerful addition to the data stack.