Category : Apache Kafka | Sub Category : Apache Kafka | By Prasad Bonam Last updated: 2023-08-05 09:29:25 Viewed : 296
Apache Kafka is a distributed streaming platform designed to handle real-time data feeds and large volumes of event data. It is built on a distributed architecture that allows for high scalability, fault-tolerance, and low-latency data processing. Kafka is often used for building real-time data pipelines, streaming analytics, log aggregation, and messaging systems.
The key components of Kafka architecture are:
Topics: Topics are the core abstraction in Kafka. They represent feeds of records, where each record is a key-value pair. Producers publish records to topics, and consumers subscribe to one or more topics to consume the data.
Partitions: Topics are divided into partitions, which are the unit of distribution in Kafka. Each partition is an ordered, immutable sequence of records. Partitions enable data parallelism and allow for horizontal scaling of data across multiple brokers.
Brokers: Brokers are the servers that make up the Kafka cluster. They store and manage the topic partitions. Producers and consumers communicate with brokers to publish and consume data.
Producers: Producers are applications or services that publish data to Kafka topics. They write records to specific topics, and Kafka handles the distribution of records across partitions.
Consumers: Consumers are applications or services that read data from Kafka topics. They subscribe to one or more topics and read records from the partitions they are assigned.
Consumer Groups: Consumers can be organized into consumer groups. Each topic partition can be consumed by only one consumer within a consumer group. This enables load balancing and parallel processing of data.
Offsets: Offsets represent the position of a consumer in a partition. Kafka stores the offset of each consumer, allowing them to resume from the last read position in case of failures or when new consumers join a consumer group.
Replication: Kafka provides replication for fault-tolerance. Each topic partition can have multiple replicas across different brokers. If a broker fails, the replicas can take over to ensure data availability.
ZooKeeper: ZooKeeper is used for managing and coordinating the Kafka brokers. It maintains metadata about brokers, topics, and partitions. Although ZooKeeper is essential for Kafka`s older versions, newer versions use the Kafka Controller and internal metadata management.
Kafka Connect: Kafka Connect is a framework for importing and exporting data to/from Kafka. It provides connectors that allow seamless integration with various external systems.
Kafka Streams: Kafka Streams is a library for building real-time stream processing applications using Kafka. It allows you to transform, aggregate, and process data streams.
Kafka`s distributed architecture and unique combination of features make it well-suited for handling real-time data streams and building data-intensive applications. Its ability to scale horizontally and handle large volumes of data has made it a popular choice for various use cases in modern data-driven applications.