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Kafka

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This guide was generated by ChatGPT. All content in this guide was generated by ChatGPT and should not be considered as professional advice or recommendations. Use at your own risk.

Introduction to Apache Kafka​

Apache Kafka is an open-source distributed streaming platform that was originally developed by LinkedIn and later became part of the Apache Software Foundation. It is designed to handle large amounts of data in real-time, making it a popular choice for building data pipelines, messaging systems, and stream processing applications.

Kafka is built on the publish-subscribe messaging model, where producers publish messages to topics and consumers subscribe to those topics to receive the messages. Kafka is distributed in nature, meaning that it can scale horizontally across multiple servers, which enables it to handle large amounts of data and provide high availability and fault tolerance.

Kafka Architecture​

Kafka has a distributed architecture that consists of the following components:

Topics​

A topic is a category or feed name to which messages are published by producers. Topics are partitioned for scalability and replication for fault tolerance. Each partition of a topic is stored on a separate broker.

Brokers​

A broker is a Kafka server that stores messages in partitions. Brokers can be added or removed as needed to scale the Kafka cluster.

Producers​

A producer is an application that publishes messages to a Kafka topic. Producers can send messages synchronously or asynchronously.

Consumers​

A consumer is an application that subscribes to a Kafka topic and reads messages from the partitions. Consumers can read messages either from the beginning or from a specific offset.

Consumer Groups​

A consumer group is a set of consumers that work together to consume messages from one or more topics. Each consumer in a group reads from a different partition of the topic.

ZooKeeper​

ZooKeeper is a centralized service for managing and coordinating distributed systems. Kafka uses ZooKeeper to store metadata about brokers, topics, and partitions.

Kafka Use Cases​

Kafka can be used for a variety of use cases, including:

  • Messaging: Kafka can be used as a messaging system for asynchronous communication between microservices, applications, and systems.
  • Stream Processing: Kafka can be used for real-time stream processing, such as filtering, aggregating, and transforming data streams.
  • Data Pipelines: Kafka can be used to build data pipelines for ingesting, processing, and storing data in real-time.
  • Log Aggregation: Kafka can be used for centralized log aggregation, where logs from different systems and applications are collected and stored in Kafka.
  • Metrics Collection: Kafka can be used to collect metrics and telemetry data from different systems and applications.

Kafka Ecosystem​

Kafka has a rich ecosystem of tools and frameworks that extend its functionality, such as:

  • Kafka Connect: a framework for building and running connectors that move data between Kafka and external systems.
  • Kafka Streams: a library for building stream processing applications using Kafka.
  • KSQL: a streaming SQL engine for processing data streams in real-time.
  • Confluent Platform: a complete distribution of Kafka with additional enterprise features and tools.

Conclusion​

Apache Kafka is a powerful distributed streaming platform that enables real-time data processing, messaging, and stream processing at scale. With its rich ecosystem of tools and frameworks, Kafka has become a popular choice for building modern data pipelines and applications.

References​