Symphony of Data: Orchestrating the Dynamics of CDC and ETL/ELT


In the world of handling and managing data, businesses often face the challenge of choosing the best way to move and change their data. Two common methods used for this are Change Data Capture (CDC) and Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT). Each method has its own benefits and drawbacks, and knowing when to use CDC versus ETL/ELT can greatly impact how well your data processes work. Let’s explore each method in simpler terms and discuss when one might be better than the other.

Understanding CDC:

Change Data Capture (CDC) is a way to keep track of changes to data as they happen. It focuses only on the new changes since the last update, which helps businesses update their data quickly. CDC watches for changes in the data at its source and then sends these changes to where they need to go, without moving the whole dataset each time.

Best Times to Use CDC:

    • Real-time Data: CDC is great when you need up-to-date information for making decisions quickly.
    • Large Datasets: It reduces the work of moving huge amounts of data by focusing only on the changes.
    • Transactional Systems: Systems with lots of transactions, like financial or e-commerce platforms, benefit from CDC without slowing down the system.
    • Data Warehousing and Data Lakes: CDC streamlines the process of filling data warehouses or lakes by including only relevant changes, saving time and storage space.

Understanding ETL/ELT:

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are traditional ways to bring together data from different places, change it to a standard format, and then put it where it needs to go. ETL changes the data before it gets to its destination, while ELT changes the data after it arrives.

Best Times to Use ETL/ELT:

    • Batch Processing: ETL/ELT works well when data can be transformed and loaded in batches rather than real-time.
    • Complex Transformations: For tasks like cleaning data, combining datasets, or doing detailed transformations, ETL/ELT is flexible and effective.
    • Historical Data: When old data needs to be integrated with new data, ETL/ELT allows for comprehensive analysis spanning different time periods.
    • Data Quality: Organizations focused on data quality often prefer ETL/ELT as it allows thorough data cleaning before loading.

Making the Right Choice:

Choosing between CDC and ETL/ELT depends on what you need for your data project. CDC is great for quick updates and saving time, while ETL/ELT is better for complex data changes and ensuring data quality. Sometimes, using a bit of both methods can give you the best of both worlds.

By carefully thinking about what you need and understanding what each method offers, you can make smart choices that improve how your data is handled, leading to better insights and decisions for your business.

Mastering Data Integration:

Navigating data integration means knowing when to use CDC for real-time updates or ETL/ELT for detailed data transformations. Understanding each method’s strengths helps you shape your data strategy to meet your business’s changing needs. Are you ready to fine-tune your data management to get the most out of your information?

For expert guidance on optimizing your data integration strategies and harnessing the power of Change Data Capture (CDC), Extract, Transform, Load (ETL), and Extract, Load, Transform (ELT), visit Newt Global’s website at For personalized consultations and inquiries, contact us at

Unlock the full potential of your data migration with Newt Global DMAP, a world-class product that enables mass migration of Oracle Databases to cloud-native PostgreSQL faster, better, and cheaper. Take the next step towards seamless data integration and transformation today!