Streaming data integration supports enriched, reusable, canonical streams that can be transformed, shared ,or replicated to different destinations, not just one.

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Streaming data integration with Kafka differs fundamentally from traditional ETL by creating reusable, canonical data streams that can serve multiple destinations in real time, rather than point-to-point batch transfers optimized for single destinations. While ETL excels in simplicity and wide connector coverage, it creates data silos, requires separate connections for each source-destination pair, and relies on batch processing with higher latency. Kafka's streaming approach enables one-to-many and many-to-one data flows, supports both real-time and batch processing, and promotes shareable data products across the enterprise. The streaming model allows data to remain in motion and be consumed by multiple systems (databases, data warehouses, lakehouses) without creating isolated copies, though it requires more infrastructure setup than traditional ETL tools.

Streaming Data Integration with Apache Kafka® vs ETL

What Becomes Possible With Streaming Data Integration