![]() ![]() To comply with strict data compliance requirements and heavy penalties for noncompliance, it is essential to save a history of changes made to your data. Analytics dashboardsĬDC can be used to feed data changes to analytics dashboards-for purposes such as business intelligence-to support time-sensitive decision making. This way, CDC can provide multiple distributed (and even siloed) teams with access to the same up-to-date data. Data replicationĬDC can be used for data replication to multiple databases, data lakes, or data warehouses, to ensure each resource has the latest version of the data. Microservices integrationĬDC can be used to sync microservices with traditional, monolithic applications, enabling smooth transfer of data changes from legacy systems to microservices-based applications. The following examples represent some of the varied use cases for change data capture. Because Kafka messaging is asynchronous, events are decoupled from the consumers, allowing for more reliable delivery of all changes. It's designed to handle data streams from multiple sources and deliver the data to multiple destinations, with high throughput and scalability.Ĭhange data capture ensures the events transmitted by Kafka are consistent with the changes in the original source system, or database. ![]() Kafka is a distributed streaming platform that can publish, subscribe to, store, and process streams of events, in real-time. ![]() The most efficient way to accomplish this is by treating the changes as events-as in an event-driven architecture (EDA)-and sending them asynchronously.Īpache Kafka is the ideal way to provide asynchronous communication between the database and the consumers of the data that require a high-volume, replayable consumption pattern. While CDC captures database changes, it still requires a messaging service to deliver those change notifications to the applicable systems and applications. For new deployments, CDC enables the use of useful patterns and schema like the "outbox," which allows microservices to exchange the consolidated data from a database transaction. Using CDC, enterprises can continue to use their legacy databases, while still making use of data through emerging technologies. In modern microservices-driven architectures, CDC has gained new importance by providing an indispensable bridge to connect traditional databases with cloud-native, event-driven architectures. In this way, CDC ensures that all interested parties of a particular data set are accurately informed of the change and can react accordingly, either refreshing their own version of the data or by triggering business processes. The change notifications are emitted in the same order they were made in the original database. CDC enables you to avoid issues like dual writes to, instead, update resources concurrently and accurately.ĬDC accomplishes this by tracking row-level changes in database source tables-categorized as insert, update, and delete events-and then making those change notifications available to any other systems or services that rely on the same data. However, trying to consistently write this changed data to more than one target introduces many challenges and coordination overhead. A simple approach would require upgrading your applications to update those resources at the same time. When updating a source database-often a relational database such as Oracle, Microsoft SQL Server, Postgres, or mysql-you may need to update multiple related resources such as a cache and a search index. ![]()
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