Apache Kafka is an open-source stream-processing software platform developed by the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Kafka is fundamentally designed to allow a single cluster to serve as the central data backbone for a large organization.
Apache Kafka was originally developed by LinkedIn and later became part of the Apache Software Foundation in 2011. It is widely recognized for its high performance, scalability, and fault tolerance. Kafka is used primarily for building real-time streaming data pipelines and applications that adapt to data streams. Its capabilities make it an essential tool for data-intensive applications across a wide range of industries.
Kafka runs as a cluster on one or more servers that can span multiple datacenters. The Kafka cluster stores streams of records in categories called topics.
One of Kafka’s key features is its ability to handle high volumes of data while maintaining low latency. This is achieved through the efficient handling of data and the use of a simplistic routing approach.
Kafka clusters can be expanded without downtime. New nodes can be added and data can be rebalanced among these nodes as they join the cluster. This allows Kafka to grow with the needs of its applications.
Kafka is designed to be fault-tolerant. It replicates data and can handle failures of nodes in the cluster without losing data.
Kafka uses a distributed commit log, meaning messages persist on disk as fast as possible, providing intra-cluster replication and hence durability. This ensures that data is not lost and can survive broker failures.
Kafka is capable of handling real-time data feeds with effectively no lag. This is crucial for applications that rely on timely and accurate data for processing.
Kafka is widely used as a replacement for more traditional message brokers, like AMQP and JMS, due to its higher throughput, reliability, and replication.
Its ability to handle high volumes of data in real-time makes it ideal for activity tracking applications. Kafka can collect user activity data from websites and apps in real time.
Kafka can aggregate statistics from distributed applications to produce centralized feeds of operational data.
With Kafka, you can process streams of data in real time. This is useful for real-time analytics and monitoring applications.
Kafka can be used for event sourcing, which involves capturing all changes to an application state as a sequence of events.
Kafka is designed to handle real-time data feeds efficiently due to its ability to provide high throughput, low latency, and built-in partitioning, replication, and fault tolerance.
Kafka ensures data durability through its use of a distributed commit log. Each message is replicated within the cluster to prevent data loss in case of hardware failure.
Yes, while Kafka is often used for real-time streaming, it can also be configured to handle batch processing, making it a versatile solution for various data processing needs.
In a microservices architecture, Kafka often serves as the backbone for messaging, providing a robust and scalable platform for services to communicate with each other.
Kafka offers better throughput, built-in partitioning, replication, and fault tolerance which makes it more suitable for large-scale message processing applications compared to traditional messaging systems.
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