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Real-time data streaming with Apache Kafka

The Open-Source-solution Apache Kafka® was originally developed at LinkedIn and enables scalable real-time data streaming combined with persistent storage. In 2014, the developers of Apache Kafka® founded the company Confluent from LinkedIn. Since then, Confluent, as the pioneer of the event streaming platform, has been driving the evolution of Apache Kafka® (80% of the commits come from within the company), as well as the vision of a central nervous system in the enterprise, enabled by a complete streaming platform.


Read more about Real-time, highly scalable data processing - speeding it up with Apache Kafka® in our whitepaper

Scenarios for usage of real-time data

  • Publishing and subscribing to data streams (messaging system)

  • Further processing of data streams

  • Loading / exporting data from / into third-party systems

  • Saving data streams

Application cases from the branches: What do ING, Lyft, Audi, Bosch or JP Morgan Chase have in common? The economic success of these companies can be based not least on the fact that crucial findings can be drawn quickly from large amounts of data.

Connected Car Services, for example, are to deliver vehicle data for predictive maintenance or for processing orders and delivering new vehicle features in real time (e.g. engine performance upgrade). This event data is streamed to a wide variety of consumers, such as analytics applications, accounting, and other platforms. Since the generation of the data can be tracked in real time and thus adjusted, manufacturing in Apache Kafka® not only reduces maintenance costs, but also optimizes end products. In finance, it is increasingly difficult for a vendor to gain a comprehensive view of the customer's activities because of the variety of customer access, devices, and other interaction opportunities. A streaming platform supports financial service providers in particular with the following challenges: Fraud detection, Cost saving , Customer 360, Marketing / Sales Event streaming also offers many valuable benefits for the retail industry in terms of optimizing the transmission, processing and evaluation of data streams in real time.

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How to use highly scalable data processing in real-time

  1. The new paradigm

    1. Data streaming solves business problems

  2. Confluent Platform:

    1. The new way: Apache Kafka® as the core of the data streaming platform

  3. Application cases from the branches:

    1. Added value through the use of an data streaming platform

  4. Conclusion