An open-source distributed streaming system called Apache Kafka is used for large-scale data integration, real-time data pipelines, and, stream processing. Kafka, which was initially developed by LinkedIn in 2011 to handle real-time data streams, swiftly developed from a messaging queue to a fully-fledged event streaming platform with the ability to process over 1 million messages per second, or billions of messages per day. Confluent now oversees Apache Kafka maintenance on behalf of the Apache Software Foundation.,
Apache Kafka is used by organizations including LinkedIn, Pinterest, adidas, Netflix, Cloudflare, AirBnB, Oracle, SalesForce, Tencent, Yahoo, and Twitter. Here, at https://kafka.apache.org/powered-by, you may view the complete list.
What is Kafka Used For?
Kafka Streams API is a powerful, lightweight library that allows for on-the-fly processing, letting you aggregate, create windowing parameters, perform joins of data within a stream, and more. Perhaps best of all, it is built as a Java application on top of Kafka.
Durable and Persistent Storage
Apache Kafka, an abstraction of a distributed commit log typically seen in distributed databases, enables long-term storage. Kafka can serve as a “source of truth,” distributing data to several nodes for a highly available deployment within a single data center or across different availability zones.
Publish and Subscribe
The basic, immutable commit log is at its center, and from there you can subscribe to it and broadcast data to any number of systems or real-time apps. Kafka, unlike messaging queues, is a highly scalable, fault-tolerant distributed system that can be used to manage passenger and driver matching at Uber, and perform numerous real-time services across all of LinkedIn. Because of its exceptional performance, it is ideal for scaling from a single app to company-wide use.
Kafka can process millions of messages per second and can handle high-volume, high-speed data.
Up to 1,000 brokers, trillions of messages per day, petabytes of data, and hundreds of thousands of partitions can be handled by Kafka clusters. Storage and processing should be elastic in size.
can send this large amount of messages with latencies as little as 2 ms using a cluster of devices.
Safely, securely store streams of data in a distributed, durable, reliable, fault-tolerant cluster
Extend clusters efficiently over availability zones or connect clusters across geographic regions, making Kafka highly available and fault tolerant with no risk of data loss.
Aspirants should have a basic understanding of Java programming.