Ingestion Methods—CDC
Explore the concept of Change Data Capture (CDC) and its importance in real-time data ingestion for businesses. Learn the three main CDC approaches—time-based, trigger-based, and log-based—and how each works to capture and propagate database changes efficiently. Understand practical applications of CDC including data warehouse loading, real-time processing frameworks, and data synchronization across systems. Gain hands-on insight into building a Postgres audit trigger in a cloud environment to monitor data changes continuously.
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In some cases, real-time data ingestion is important for businesses across various industries. It allows e-commerce and retail to have more accurate and rapid demand forecasts and adjust pricing quickly. Real-time ingestion can provide real-time IoT sensor alerts that help companies reduce downtime and optimize product performance.
Let’s look at a (near) real-time data ingestion method, change data capture (CDC), and its three different approaches.
Change data capture
Change data capture (CDC) is the process of ingesting changes from a source database. It provides real-time or near real-time data movement by moving data continuously as new database events occur. CDC is a very efficient way to move data across a wide area network, perfect for the cloud. There are many use cases for CDC. Here are a few examples:
Load real-time data into a data warehouse. Operational databases are not good for heavy analytical workloads. Therefore, operational data should be moved to a data warehouse to perform analysis. The traditional batch-based ETL has a latency issue. But with CDC, we can capture source data changes as they occur and deliver them to the data warehouse in real time.
Load real-time data into real-time frameworks. Database events can be delivered to real-time process engines like Apache Kafka and Apache Flink to apply transformations and provide real-time insights.
Data replication/synchronization. The source ...