![]() ![]() ![]() The first step in the ETL process is to extract data. The ETL process comprises 3 main stages: Extraction It’s a structured procedure that involves 3 separate stages. Let’s understand each of these and talk about the common challenges you might face when you set up ETL in your data system. It’s crucial for businesses that rely on data analysis to uncover insights, identify trends, and detect patterns that guide strategic decision-making. However, the log-based CDC method is often regarded as the most efficient method, especially when dealing with large volumes of data.Ĭheck out our CDC Guides for popular databases:ĮTL, short for Extract, Transform, and Load, is a fundamental process in data management. The choice of method depends on your specific needs and the nature of your data. Some are easier to implement, while others offer more flexibility. The trigger then writes the changes into another table, typically called a shadow or staging table.Įach method has its strengths and weaknesses. For every insert, update, and delete operation, a trigger is fired, capturing the change. ![]() Trigger-based CDC: In this method, database triggers are used to identify changes.The CDC tool reads this log to track all changes. The transaction log contains a complete list of all data changes in their exact order of application. Log-based CDC: This method involves reading the transaction logs of the source database.The CDC tool checks these timestamps to identify the changes. Most systems record when a row was created and most recently modified. Timestamp-based CDC: In timestamp-based CDC, timestamps are used to track changes.Table Delta CDC: This method uses a tool or script to compare the data in two tables and spot the differences.Let’s take a look at the most common ones: There are different methods to capture changes in data. Change Delivery: After recording the change, the CDC tool reflects it in your data warehouse or other systems that you use.ĬDC isn’t a one-size-fits-all process.It records what was changed, how it was changed, and when it was changed. Change Recording: Once a change is detected, the CDC tool captures the details.When a change occurs, the CDC tool spots it. They keep an eye on the transaction log where the database records all changes. Change Detection: CDC tools monitor your database.On the other hand, CDC operates in real-time and ensures that your data is always current, no matter how often changes happen. Traditional methods of data integration, like batch processing, can struggle to keep up with the constant flow of changes. When a change is detected, CDC captures the details and transfers the changes to your data warehouses, data lakes, or other systems that use the data.ĬDC helps keep your data up-to-date for business analytics and decision-making. Understanding Change Data Capture & How It WorksĬhange Data Capture (CDC) identifies and captures changes made to data and keeps track of all the modifications. In the next 10 minutes, you will learn what CDC and ETL really do, the steps to make them work well together, and the benefits of incorporating CDC in your ETL pipelines.īy the end of this guide, you’ll be all set to use CDC to optimize your ETL pipelines and make your data management more efficient and effortless. Addressing these challenges requires a clear understanding of CDC and ETL processes and knowledge of key steps needed for incorporating CDC into ETL workflows. A CDC ETL integration identifies and captures data changes as they occur, eliminating the need for full data loads and providing near real-time updates.īut integrating ETL processes with CDC often comes with challenges like complexity in implementation, data consistency issues, and managing the performance of data pipelines. Traditional Extract, Transform, Load (ETL) processes, while effective in their own right, often fall short when it comes to real-time data integration and scalability. ![]()
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