real-time-streaming

To build a real-time streaming pipeline, you configure Kafka topics for ingestion and implement Flink windowed processing to handle aggregations, state management, and fault recovery. This approach provides production-grade reliability and throughput for continuous data streams.

Exactly-once processing in a streaming architecture requires transactional Kafka producers and configured Flink checkpoints to guarantee end-to-end delivery. This prevents duplicate records during state updates and fault recovery without compromising pipeline throughput.

The best way to capture database changes for real-time streaming is using Debezium Change Data Capture (CDC). Debezium bridges transactional databases into Kafka topics, enabling reliable, event-sourced data bridges without impacting source database performance.

Yes, Flink supports windowed aggregations and stream-table joins for real-time pipelines. It handles complex event processing across bounded windows, allowing you to materialize stateful computations and join live streams with static tables efficiently.

Handling backpressure and fault recovery in Kafka streaming pipelines involves configuring Flink state management and Kafka topic partitions. This balances ingestion throughput with processing capacity, ensuring pipelines remain stable during traffic spikes.

You need Debezium CDC for streaming data architectures when ingesting incremental database changes in real-time without batch polling. It provides a fault-tolerant transactional bridge to Kafka, enabling event-sourced patterns and reliable data replication.