Kafka
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Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Above is a snapshot of the number of top-ten largest companies using Kafka, per-industry.
Apache Kafka is a data streaming system used for real-time data pipelines, data integration, and event-driven systems. Learn how Kafka works with examples and use cases.
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
To process streams of events as they occur or retrospectively. And all this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka can be deployed on bare-metal hardware, virtual machines, and containers, and on-premises as well as in the cloud.
Apache Kafka also works with external stream processing systems such as Apache Apex, Apache Beam, Apache Flink, Apache Spark, Apache Storm, and Apache NiFi. Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes.
Franz Kafka [b] (3 July 1883 – 3 June 1924) was a German-language novelist and writer from Prague. He is widely regarded as one of the major figures of 20th-century literature. His work fuses elements of realism and the fantastic. [4]
Apache Kafka is a distributed data streaming platform used for real-time data pipelines, integration, stream processing, and more. Learn how Kafka works and how it's used with examples.
Building real-time streaming applications that transform or react to the streams of data. First a few concepts: Kafka is run as a cluster on one or more servers that can span multiple datacenters. The Kafka cluster stores streams of records in categories called topics.
May 11, 2024 · As we have found out, Kafka uses topic partitions to allow parallel message delivery and load balancing of consumers. But Kafka itself must be scalable and fault-tolerant. So we typically do not use a single Kafka Broker, but a Cluster of multiple brokers.
As you explore Kafka patterns, and detailed examples, you’ll learn how to write reliable, event-driven microservices and build real-time apps and data pipelines. The guide helps architects, developers, and operators alike gain the Kafka expertise needed to:
