![]() ![]() This is an effective methodology, but time consuming and resource intensive. ![]() ![]() This method compares a snapshot of table data with a previous snapshot, row by row, column by column, to determine system changes. Timestamps, table differencing, event programming/ triggers, and log scanners are just some of the ways that data changes can be detected. The method by which changes are logged can be a key difference in Change Data Capture methodology. Change Data Capture is typically associated with SQL or MySQL Server workloads, but can theoretically be applied to any type of data. In other words, there is a source and a target, even if technically it can be physically within the same system. In the simplest form, one computer system obtains or has data changed from a previously defined point of time, and another system (or sub-system) must take action based on this change in data. By focusing on data changes, data validation and transformation efforts can be more focused, as well as bringing teams together on a shared definition of what constitutes data value. CDC can improve efficiency for cloud migration, data warehousing, database queries, and more.īesides efficiency, CDC is also a way to unsilo and democratize an organization’s data, so that business value can be derived, rather than simply gathering the data for retention purposes. By capturing a baseline snapshot, monitoring data delivery via logs, and replicating changes as they occur, system resources are not wasted on rewriting or transferring unnecessary data. Whether capturing streaming data, using an ETL (extract, transform, load) solution, or any high velocity data management solution, Change Data Capture (CDC) can be an excellent fit to achieve low-latency, reliable, and scalable data replication. By focusing efforts on the changed data, efficiency is increased, both in terms of system resources for obtaining the data, and in terms of obtaining actionable data quickly. Rather than acting upon an entire dataset, the database or data warehouse can limit resource expenditure to a subset of data – the data that has changed. The basic concept is that software processes are used to track and identify changes to data from a defined point in time, and this changed data is replicated to another system, such as a data warehouse, allowing the enterprise to act upon those changes. Change Data Capture is not a new concept, and has been a part of database and data warehouse management for nearly as long as they have been around. ![]()
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