![]() ![]() Others don't, and in-depth expertise is required to get changes out.įew vendors, including Fivetran, provide log-based CDC through so-called binary log readers. Some database technologies provide an API for log-based CDC. Fundamentally, all committed and recoverable changes can be found in the database transaction logs.Ī log-based capture mechanism parses the changes from the transaction log, asynchronously from the transactions submitting the changes. In the case of a system or database crash, the transaction log enables loss-less database recovery. Most databases built for online transaction processing (OLTP) use a transaction log to record changes. These include log-based CDC, trigger-based CDC, CDC based on timestamps and difference-based CDC. ![]() There are multiple types of change data capture that can be used for data processing from a database. This is an especially invaluable benefit for time-sensitive applications and environments. Synchronization across multiple data systemsĪs an extension of the above, CDC’s real-time data transfer also allows multiple data systems to stay synchronized regardless of where they’re located. This also allows for real-time analytics, synchronization and other applications across distributed systems. Faster database migrations with no downtimeĬDC’s ability to move data quickly and efficiently also allows for real-time database migrations without any of the downtime. This allows it to work in real time and greatly reduces the impact on system resources that would otherwise be over-consumed during bulk loading. The key principle of CDC is that it transfers data in tiny increments rather than bulk loads. This also reduces the impact on source extracts, which no longer need to be refreshed all at once. With no more batch windows or bulk loading, ETL occurs in real time and with better communication between data repositories and sources. Real-time operations (i.e., no more bulk loading) Here are a few ways CDC’s efficiencies can benefit your data integrations. If anything, load occurs before transform, as most cloud-based target repositories (e.g., data warehouse, data lake, etc.) handle the transformation. LoadĪs you may have gathered in the “transform” section, load and transform occur almost simultaneously with CDC. With the ever-increasing size of modern data, this approach isn’t just more efficient - it’s necessary to keep up. Instead, data is continuously loaded as the source changes and then transformed in the target data repository. While this is still true with CDC, it doesn’t attempt to transform large batches of data at once. Traditionally, entire data sets need to be transformed using ETL tools to match the structure and format of the target table or repository before loading. TransformĬDC also presents new efficiencies at the transform stage. CDC sidesteps this problem by maintaining a data stream in real-time. While this certainly gets the job done, it quickly becomes inefficient as source databases are continuously updated.īy having to refresh a replica of source tables every time, the target table may not accurately reflect the current state of the source application. Traditionally, this process is performed in batches, where a single database query extracts a large amount of data in bulk. Extractĭuring the extract stage, CDC extracts data in real-time (or near-real time) and provides a continuous stream of change data. Let’s walk through and see what CDC looks like throughout each stage of the ETL process. ![]()
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