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Apache Kafka Technology

Today, every company that takes data collection and processing seriously has a message broker. If a company places services on its infrastructure, not in the cloud, it most likely uses Apache Kafka as an intermediary.

Kafka has already become popular. Most IT companies are already actively and successfully using it.

So, what is Apache Kafka, and what role does this framework play in data processing systems?

Kafka is a service that allows developers to transfer messages between different systems in real-time and with high bandwidth. It is used for various purposes such as data transfer to storage, streaming analytics, the interaction between services, etc.

It should be noted that this solution is not for small systems. Not every project grows to loads under which this service becomes really needed. But knowing the primary uses of Apache Kafka allows businesses to lay the necessary structures in the backend architecture, which, if necessary, are correctly scaled up to accommodate their business growth.

Apache Kafka for data engineering

Kafka allows users to process big data in continuous information flows and store them without the risk of loss. Apache Kafka can also act as a binding element of the Big Data system, ensuring the interaction of separated microservices.

Here are some arguments why data engineers need Apache Kafka tool:

1. It allows developers to unify the data exchange protocol between different systems.

2. It acts as temporary storage between the source and the receiver.

3. It is a "buffer" for the load. The receiver system may fall under load if the source suddenly gives out a large amount of data. But if Kafka stands between them, it will take on the load and, thanks to high fault tolerance and bandwidth, will be able to receive and store data while the receiver system subtracts it at its pace.

4. It is vital for real-time analytics because of allowing developers to deliver data with very low delays.

Where Kafka is Used

The essential purpose of Kafka is the centralized collection, processing, secure storage, and transmission of a large number of messages from separate services. This distributed, horizontally scalable platform is usually used where there is a lot of big unstructured data:

  • Large-scale IoT/IIoT systems;

  • Analytics systems;

  • Financial systems;

  • Social media;

  • Geopositioning systems;

  • Online games and others.

The service allows developers to build a data pipeline to use machine learning algorithms to extract information valuable to businesses from raw information.

Kafka has become an obligatory element for launching many data processing architectures. This tool must be used if:

  • you have a complex data exchange topology that includes a large number of sources and receivers based on various technologies;

  • you need to provide real-time analytics;

  • you want to improve the reliability of your data delivery system.

Every year, the role of data streaming and real-time analytics is only increasing. The need to introduce Kafka or a similar broker into the data processing system will become more and more relevant.

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