6/15/2023 0 Comments Deleting poker copilot database![]() But batch data pulling requires additional computing, provides insufficient inputs on the history of deleted rows, and entails higher latencies. ![]() Traditionally, doing this has meant batch data replication, executing once or several times a day. Whenever there’s a change or update in the database, we also need to be able to sync between the two in as close to real time as possible, and with as little friction and complexity as possible. Why CDC in the first place? The need for replication speedĪs data engineers and analysts, we need to be able to move data from our relational databases (point A), such as SQL Server or MySQL, to data warehouses, data lakes, or other target databases (point B). And then we’ll get into why the platform we chose wasn’t optimal for our needs, and what we had to do to make CDC work for us and our customers. If you’re looking to deploy a CDC platform, then it’s worthwhile to jot down the challenges we faced, and learn from our experience, so you can be more confident in the solution you choose and that it will support your needs.īut first, let’s take a quick look at why CDC is so important in the first place. There were a number of hurdles that we had to overcome, and which ultimately made this option a non-starter. What we found is a well-regarded open-source CDC solution. So we went out looking for the best solution to help our customers take care of these updates and changes, i.e., with change data capture (CDC). What we also need to be able to do is to move updates and changes from point A to point B in the most efficient and fastest way possible. So, it’s no surprise that part of what we need to do is to not only move data from point A to point B. Once upon a time, in a data pipeline not so far awayĪt Rivery, we are all about data, specifically managing the data pipeline lifecycle.
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