![]() With CDC, however, it is possible to replicate only specific tables or columns, which can be useful in scenarios where only a subset of the data is needed in the target database.ĬDC is also useful for data integration scenarios. With traditional data replication methods, the entire source database is typically replicated to the target database. This is important for scenarios where the target database is used for reporting or analytics purposes, as it ensures that the data in the target database is always up-to-date.ĬDC also allows for greater flexibility in terms of data replication. One of the main advantages is that it allows for real-time data replication, which means that data changes made to the source database are quickly reflected in the target database. The push approach is crucial for real-time data systems that need low-latency actions. To solve this problem the target has to implement a queuing system that records the received data changes. The only downside of this approach occurs when the target is not listening to the data source sending data changes. There has to be a mechanism set up to receive and interpret the data changes. Pushing data occurs when the data source sends data changes to the target to take any necessary actions. Even though it is easy to pull data this comes with the problem of latency as the target has to consistently read the data source logs. ![]() This is because pulling data changes only requires the target to read the logs of the data source to identify any changes and take any actions if data has changed. It's easier to pull data changes from the source than it is to push data changes. There are two ways CDC occurs: either data change is pushed from the source to the data warehouse or a data change is pulled from the source by the target. This process is often referred to as "log-based CDC." CDC software reads these logs and captures the changes, which are then applied to the target database. These logs contain a record of all changes made to the database, including insertions, updates, and deletions. This technology is commonly used in data warehousing, business intelligence, and data integration scenarios.ĬDC works by monitoring the transaction logs of a source database. It is used to replicate changes made to a source database to one or more target databases, in real-time. What is Change Data Capture (CDC)Ĭhange Data Capture (CDC) is a technology used to track and capture changes made to a database. In this article, you will learn what Change Data Capture (CDC) is, its use cases, and mechanisms. It is important to know when data changes in order to invest well.ĭata has to be updated even when it was changed a few seconds ago there is a mechanism called Change Data Capture (CDC) that spots any data changes in the data source and triggers an update to the target. Netflix stock was down by 65% in 2022 July and took a turn at the end of the year when it gained 7 million subscribers. Data that is not updated is dangerous and misleading when being used to plot the next technical move. Real-time analytics and up-to-date data is the oxygen of effective decision making. Industries change in split seconds and you have to know which change matters. Making data driven decisions is not effective when data being used is not fresh and exhibits low quality. Failure to notice new trends and patterns is caused by the inability to spot new changes in trend data and pattern adjustments. The main reason why top companies don't stay at the top forever is because they can't keep up with new trends.
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