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 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
The new paradigm
Data streaming solves business problems
The new way: Apache Kafka® as the core of the data streaming platform
Application cases from the branches:
Added value through the use of an data streaming platform