Apache Kafka is an open-source stream processing platform that was initially developed by LinkedIn in 2011. It is written in Scala and Java, and it is designed to handle high volume, high throughput, and low latency data streams.
The problem that Kafka was designed to solve is the need to process and analyze large volumes of real-time data in a distributed environment. Traditional solutions for this problem involved using complex and expensive data integration and ETL (extract, transform, load) tools, which were not always able to keep up with the high volume and velocity of data.
Kafka addresses this problem by providing a distributed, publish-subscribe messaging system that is designed to be fast, scalable, and durable. It is used to build real-time data pipelines and streaming applications, and it can process millions of messages per second with minimal overhead.
One of the key features of Kafka is its ability to handle data streams in a highly distributed and fault-tolerant manner. It uses a distributed commit log to store data, which allows it to handle data streams in a reliable and consistent way even if individual nodes in the cluster fail.
In addition to its real-time streaming capabilities, Kafka also has support for batch processing of data, which makes it a powerful tool for data integration and ETL. It can be used to move data between systems, perform data transformations, and load data into data warehouses and other storage systems.
It is a powerful and flexible tool for processing and analyzing real-time data streams in a distributed environment. It is used by a wide range of organizations in a variety of industries, including finance, e-commerce, and social media.
Apache Kafka is a stream processing platform that provides a range of APIs for building real-time data pipelines and streaming applications. These APIs include:
Producer API: This API allows applications to send data to Kafka topics. Producers are responsible for sending data to Kafka brokers, which then store the data in a distributed commit log.
Consumer API: This API allows applications to read data from Kafka topics. Consumers are responsible for pulling data from Kafka brokers and processing it.
KSQL API: KSQL is a SQL-like language for stream processing on Kafka. It allows developers to build stream processing applications using familiar SQL syntax, and it provides a range of functions for performing operations such as filtering, aggregating, and joining streams of data.
Connect API: The Connect API allows developers to build reusable connectors that can move data in and out of Kafka. These connectors can be used to integrate Kafka with external systems, such as databases, message queues, and file systems.
Sink API: The Sink API is a part of the Connect API that allows developers to write data from Kafka to external systems.
Zookeeper API: Apache Kafka relies on Apache Zookeeper for coordination between brokers and clients. Zookeeper is a distributed coordination service that helps to manage the Kafka cluster and provide information about the status of Kafka brokers and topics.
Broker API: The Broker API is a set of APIs that are used by Kafka brokers to manage and maintain the Kafka cluster. These APIs are not generally exposed to users, but they are used by the Kafka software to handle tasks such as leader election, cluster membership, and data replication.
Overall, these APIs provide a range of capabilities for building real-time data pipelines and streaming applications with Apache Kafka. They allow developers to send and receive data from Kafka topics, process and analyze streams of data, and integrate Kafka with external systems.