If you're looking for a better way to organize your data and ensure it stays up-to-date, you need to start utilizing CDC processes today. Change data capture uses various techniques to detect changes made in source tables and databases in real-time. Read on to learn more about change data capture and how it can be implemented to better serve your business.  

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What Is Change Data Capture? 

Change data capture, or CDC, is a set of software processes that identify changes in source tables and databases, often tracking and updating those changes in real-time. As CDC works in real-time movement, change data capture is an ideal solution for businesses looking to work with their data more efficiently. Check out more about CDC and change data types below. 

Related Reading: What is Change Data Capture?

Types of Change Data Capture 

Essentially, there are two main types of change data capture. CDC is performed through log-based CDC or trigger-based CDC. 

Log-Based CDC 

In log-based CDC, the change data capture looks at the database's transaction log. In this process, the change data capture solution reads all files in the log to uncover any source system changes. After examining all files, the CDC solution completes data replication of these source changes to the target data store.

Pros of log-based CDC:

  • High Reliability and Accuracy 
  • Minimal Impact on Production Database System 
  • Easy to Monitor Changes in Real-Time 
  • Does Not Require Any Change to the Production Database System's Schemas or Need to Add Additional Tables 

Cons of log-based CDC:

  • Works Only With Databases That Support Log-Based CDC 
  • High Complexity Overall

Trigger-Based CDC

In trigger-based CDC, the change data capture utilizes database triggers. In this process, the change data capture solution runs in response to another event. With trigger-based CDC, overhead that results from extracting changes is often decreased. However, trigger-based CDC can also add overhead to source systems as they require a certain amount of run time to complete after each time they are activated

Pros of trigger-based CDC:

  • Implementation is Easy
  • Changes Can Be Made Quickly 
  • Detailed Logs of All Transactions Can Be Viewed in the Shadow Tables Can 
  • Receives Direct Support in the SQL API for Some Databases 

Cons of trigger-based CDC:

  • Too Many Activated Triggers May Overload the System
  • During Certain Operations Some Triggers May Be Disabled 
  • Significantly Reduces the Overall Performance of the Database by Requiring Multiple Writes to a Database Every Time A Row is Inserted, Updated, or Deleted

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Implementing Change Data Capture 

The whole idea behind change data capture solutions is that they eliminate the need to copy entire databases every time a table needs to be updated. Instead of entire database updates, CDC allows for datasets to be updated incrementally. For implementing change data capture, there are different techniques that can be utilized. Discover the top CDC implementation techniques below: 

  • Timestamp Based Technique: With the timestamp-based technique, a timestamp field in the source is used to identify and extract changes made in data sets. 
  • Log-Based Technique: The log-based technique uses a transactional database to read changes from the log. Any inserts, updates, or deletes made to the log are read and then applied to the target system.
  • Trigger-Based Technique: With the trigger-based technique, database triggers are used to identify changes that have occurred in the source system and then capture those changes into a target database.
  • Script-Based Technique: In the script-based technique, CDC can be coded at the application level by adding fields to the existing rows or metadata to identify that the data has been updated

Related Reading: CDC in Salesforce and How to Export Attachments

Change Data Capture and ETL 

Ultimately, implementing change data capture proves extremely useful when paired with ETL. The most common type of data integration used by companies today is ETL (Extract, Transform, Load). Through the ETL process, information is extracted from one or more data sources, processed, and then delivered to a data warehouse, lake, or other database types. 

By pairing CDC with ETL, companies can save a great deal of time, effort, and energy compared to utilizing traditional ETL systems. The major benefits of change data capture are that it can improve the time it takes to carry out data transfers and decrease the number of resources required to run the ETL process. 

Related Reading: What is ETL?

How Integrate.io Can Help

Integrate.io has all the tools for your ETL and CDC needs. Using change data capture for ETL can be a difficult process if you don't have the right tools. However, by working with Integrate.io, you gain access to the most effective CDC implementation methods and the most user-friendly ETL and data integration platform. 

Is your company in need of CDC and ETL solutions to run more efficiently? Contact our team today to schedule a 14-day demo or pilot and see how we can help you reach your goals.