π Apache Kafka Cluster Explained: Core Concepts and Architectures π
In our data-driven world, real-time processing is key! Apache Kafka, an open-source distributed streaming platform, stands out as a leading solution for handling real-time data feeds. This comprehensive guide delves into Kafka's architecture, key terminologies, and solutions to data streaming problems. π
Highlights:
πΈOrigins of Kafka: Developed by LinkedIn for scalable messaging, open-sourced in 2011.
πΈCore Functions: Real-time data processing, scalability, fault tolerance, and decoupling data streams.
πΈKey Terms: Producers, Consumers, Brokers, Topics, Partitions, Offsets, Consumer Groups, Replication.
πΈArchitecture: Traditional setup with Zookeeper and the new KRaft architecture.
πΈ Kafka with Zookeeper: Manages metadata and broker coordination.
πΈ KRaft Architecture: Integrated metadata management within Kafka using the Raft protocol, enhancing scalability and performance.
For a deeper understanding of the Raft protocol used in KRaft architecture, check out my latest post on the Raft Consensus Algorithm π β¨
Apache Kafka Cluster Explained: Core Concepts and Architectures
Top comments (0)