The target system then has to either watch for the update on the column and fetch the changed record or subscribe to the queue. Most patterns require the source system to flag that a change has happened to some data, for example by updating a specific column on a table in the database or putting the changed record onto a queue. There are many ways to implement a change data capture system. The next section will cover the positives and negatives of a number of different CDC mechanisms that utilise the push or pull approach. If latency isn’t a big issue and you need to transfer a high volume of bulk updates, then pull-based systems should be considered. The rule of thumb is that if you are looking to build a real-time data processing system then the push approach should be used. This often leads to data being pulled in batches anywhere from large batches pulled once a day to lots of small batches pulled frequently. This is because the target has to poll the source system for updates rather than being told when something has changed. The downside of the pull approach is that it often increases latency. The benefit of this is the same as the queue-based approach mentioned previously, in that if the target ever encounters an issue, because it's keeping track of what it's already pulled, it can restart and pick up where it left off without any issues. The target system is then responsible for pulling the changed data by requesting anything that it believes has changed. Pull-based systems are often a lot simpler for the source system as they often require logging that a change has happened, usually by updating a column on the table. If the target needs to stop listening to the queue, as long as it remembers where it was in the queue it can stop and restart where it left off without missing any changes. To mitigate this, queue- based systems are implemented in between the source and the target so that the source can post changes to the queue and the target reads from the queue at its own pace. The downside of the push-based approach is that if the target system is down or not listening for changes for whatever reason, they will miss changes. This approach often leads to lower latency between the source and target because as soon as the change is made the target is notified and can action it immediately, instead of polling for changes. The target system simply needs to listen out for changes and apply them instead of constantly polling the source and keeping track of what it's already captured. Push-based systems often require more work for the source system, as they need to implement a solution that understands when changes are made and send those changes in a way that the target can receive and action them. Either the source system pushes changes to the target, or the target periodically polls the source and pulls the changed data. There are two main ways for change data capture systems to operate. This post is useful for anyone who wishes to implement a change data capture system, especially in the context of keeping data in sync between two systems. This post will explain some common CDC implementations and discuss the benefits and drawbacks of using each. There are many ways to implement a change data capture system, each of which has its benefits. A common use case is to reflect the change in a different target system so that the data in the systems stay in sync. Change data capture (CDC) is the process of recognising when data has been changed in a source system so a downstream process or system can action that change.
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