Kafka-experiments
Apache Kafka is an open-source, distributed streaming platform that enables real-time processing of data streams. It is designed to handle high-throughput, low-latency processing of large volumes of data, making it well-suited for use cases such as real-time analytics, event-driven architectures, and data pipelines.
Kafka is based on a publish-subscribe model, in which producers send data to Kafka topics and consumers subscribe to those topics to receive the data. Kafka stores data in a distributed, partitioned, and replicated log structure, allowing it to scale horizontally and tolerate failures.
Kafka has a number of key features that make it a popular choice for data processing:
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Scalability: Kafka is designed to handle a large volume of data and can scale to handle millions of messages per second.
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Durability: Kafka stores messages on disk, making it possible to recover from failures and maintain data integrity.
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Low latency: Kafka is designed for low-latency processing, making it suitable for real-time applications.
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High-throughput: Kafka is able to handle high-throughput data streams, allowing it to process large amounts of data in real-time.
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Flexibility: Kafka is highly flexible and can be used for a wide range of data processing use cases, including real-time analytics, data pipelines, and event-driven architectures.
This repository contains sample projects integrating with kafka using different mechanisms available
Reference
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