![]() These triggers are procedural codes that automatically react to a certain operation in the database and activate once someone performs an insert, update, or remove operation in a database table. Some prefer more intrusive methods, like creating database triggers to identify changes. Not only that but there are many types of Change Data Capture methods, each suitable for different situations and data needs. In addition, a CDC design can be implemented within the system – physically speaking – or externally on another computer system. One system can have one or multiple CDC designs. First, it simplifies and quickens the process, and second, it provides more reliable data in the system.ĬDC can also work alongside ETL’s more modern counterpart – ELT (Extract, Load, Transform) – a more flexible process that doesn’t transform the data before loading it. However, CDC captures and delivers even the tiniest changes made to the data, step-by-step, in real-time.įor this reason, it brings many benefits to ETL pipelines. And this is where CDC comes in.īefore CDC technology, ETL could only extract data in bulk which slowed down the process and didn’t always provide accurate real-time information. But accuracy is paramount in this process as even the slightest undocumented change can influence outcomes. With the help of ETL, a data warehouse stores massive amounts of data from various sources. On the other hand, a data warehouse contains filtered and structured data and has a specific purpose, mainly for BI (Business Intelligence) activities, most notably analytics. Change Data Capture in ETL (ETL CDC)ĮTL, an acronym for Extract, Transform, Load, is a type of data pipeline that transforms extracted data before loading it to its target system, like a data warehouse or a data lake.ĭata lakes are systems that contain a large amount of raw data without any clearly defined objective. Moreover, the CDC technology is supported by multiple servers, including Microsoft’s Azure SQL Server and Oracle, making it the ideal solution for the movement of data. The best thing about CDC is that it works in real-time, allowing data analysts to indulge in the most accurate real-time data science and analytics.ĬDC creates a smooth flow and increases the system’s reliability which is especially crucial in cloud architectures or a data warehouse in general, where there is constant flow and integration of data. This record is later stored either in the same database or in external applications. Simply put, CDC looks for shifts in a database, and when it finds one, it records it. If your server currently has no problems keeping up with its load, I very much doubt you'll notice any performance problems enabling CDC for infrequent changed tables.Change data capture (CDC) is a specific technology, or a set of software design patterns, that recognizes, tracks, and delivers data changes in a database.
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