Apache Kafka is a distributed occasion streaming platform that gives high-throughput, fault-tolerance, and scalability for shared knowledge pipelines. It capabilities as a distributed log enabling producers to publish knowledge to subjects that buyers can asynchronously subscribe to. In Kafka, subjects have schemas, outlined knowledge varieties, and knowledge high quality guidelines. Kafka can retailer and course of streams of information (occasions) in a dependable and distributed method. Kafka is broadly used for constructing knowledge pipelines, streaming analytics, and event-driven architectures.
Apache Flink is a distributed stream processing framework designed for high-performance, scalable, and fault-tolerant processing of real-time and batch knowledge. Flink excels at dealing with large-scale knowledge streams with low latency and excessive throughput, making it a well-liked alternative for real-time analytics, event-driven purposes, and knowledge processing pipelines.
Flink usually integrates with Kafka, utilizing Kafka as a supply or sink for streaming knowledge. Kafka handles the ingestion and storage of occasion streams, whereas Flink processes these streams for analytics or transformations. For instance, a Flink job would possibly learn occasions from a Kafka subject, carry out aggregations, and write outcomes again to a different Kafka subject or a database.
