Apache Kafka Tutorial
Apache Kafka Tutorial
Apache Kafka is an open source and stream processing software platform, which is developed by LinkedIn and donated to the Apache Software Foundation, written in Scala and Java. Kafka is used in place of traditional message brokers like JMS and AMQP due to its higher throughput, reliability and replication.
Kafka is a distributed streaming platform which is used to publish and subscribe to the streams of records. Kafka is used for fault tolerant storage and it replicates topic log partitions to multiple servers. It is designed to allow the applications to process records as they occur.
Kafka is a fast, scalable, durable and fault-tolerant publish-subscribe messaging system, which is used in the use cases where JMS, RabbitMQ, and AMQP may not even be considered due to volume and responsiveness.
Kafka is run as a cluster on one or more servers, which can span multiple datacenters. The Kafka cluster stores the streams of records in categories called topics and each record consists of a key, a value, and a timestamp.
Kafka is used for the 2 broad classes of applications, One is building real-time streaming data pipelines which reliably get data between systems or applications and the other is building real-time streaming applications which transform or react to the streams of data.
Kafka has very low overhead because it doesn’t track the consumer behavior and delete messages that have been read. Instead, Kafka retains all the messages for an amount of time and makes the consumers responsible for tracking which messages have been read.
Kafka server stores all the incoming messages from publishers for some period of time and publishes them to a stream of data called a topic. Kafka consumers subscribe to one or more topics, and receive data as its published.
It is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers.
Kafka can be used to feed fast lane systems, real-time and operational data systems, like Storm, Flink, Spark streaming, and your services and CEP systems. Kafka is also used to stream data for batch data analysis.
Apache Kafka Tutorial
Apache Kafka Online Training from India
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