Real-time data streaming with Apache Kafka

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.

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