Streaming Data Integration with Apache Kafka® vs ETL

This post contrasts streaming data integration with ETL, arguing that Kafka's original use case of a centralized log for broadcasting and sharing data in real-time aligns with modern needs for interconnected data products. It details the limitations of ETL, such as data silos, lack of reusability, batch processing, and high latency, and highlights the advantages of streaming data integration with Kafka, including one-to-many and many-to-one source/destination support, reusable canonical streams, unification of real-time and historical data analysis, and the ability to build richly interconnected data products. It also suggests incorporating ETL best practices like intuitive workflows and reduced toil into streaming data integration.