AntStack TV Episode 5 Real-Time Data CDC and Apache Spark Essentials

 Change Data Capture (CDC) is transforming the way organizations handle real-time data updates by enabling seamless, efficient, and low-latency data synchronization between systems. CDC continuously monitors and captures changes—such as inserts, updates, and deletes—in source databases and delivers them in real time to downstream applications. This ensures that data pipelines remain accurate, up-to-date, and responsive to operational needs.



One of the key enablers of CDC is Apache Kafka, a high-throughput distributed event streaming platform. Kafka acts as a reliable backbone for transmitting CDC events from source databases to various destinations. It ensures durability, fault-tolerance, and scalability—making it ideal for real-time ingestion and distribution of massive volumes of data changes. By decoupling the data source from consumers, Kafka allows for flexible and scalable integration with analytics, monitoring, and data lake systems.


Apache Spark complements this architecture by offering powerful data processing capabilities for both batch and streaming workloads. Using Structured Streaming, Spark can consume CDC events from Kafka and process them in near real-time for analytics, transformations, or storage. Spark also supports complex operations like joins, aggregations, and machine learning at scale, enabling actionable insights with minimal latency.


Together, CDC, Kafka, and Spark form a robust ecosystem for building real-time data pipelines that support use cases like fraud detection, customer personalization, operational reporting, and system monitoring. Organizations leveraging this stack gain a competitive edge by enabling faster decision-making, improved data accuracy, and efficient system interoperability.


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