Apache Kafka is a distributed event streaming platform that provides high-throughput, fault-tolerance, and scalability for shared data pipelines. It functions as a distributed log enabling producers to publish data to topics that consumers can asynchronously subscribe to. In Kafka, topics have schemas, defined data types, and data quality rules. Kafka can store and process streams of records (events) in a reliable and distributed manner. Kafka is widely used for building data 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 data. Flink excels at handling large-scale data streams with low latency and high throughput, making it a popular choice for real-time analytics, event-driven applications, and data processing pipelines.
Flink often integrates with Kafka, using Kafka as a source or sink for streaming data. Kafka handles the ingestion and storage of event streams, while Flink processes those streams for analytics or transformations. For example, a Flink job might read events from a Kafka topic, perform aggregations, and write results back to another Kafka topic or a database.
