reactive streams

Big-Data reactive streams and OSGi

One of the basic requirement to enable big-data analytics is a rational and effective approach to data ingestion. In long running projects the need arises to evolve the domain model and this potentially affects data quality. As a consequence, the concept of versioning is crucial to keep data centric systems consistent: the importance of service dynamicity and good modularity support in a sound data ingestion workflow implementation cannot be easily overestimated.

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Reactive OSGi meets Reactive Java – Don’t cross the streams?

Data collection, filtering and processing is a growing concern for all sorts of applications. A variety of different libraries have been created, all looking to solve the same sorts of problems. The OSGi Alliance has defined the PushStream with a simple streaming API, but Reactive Streams and Java 9’s Flow API use a the Observable model. Which is better? Do I have to use OSGi if I choose Push Streams? Do I have to choose one library or can I use both?

This talk will describe the basic design differences between Push Streams and Observable streams,  showing side-by-side examples using both APIs. It will also show how one stream type can be converted into the other, and back meaning that you can always choose the appropriate tool for the job.

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Reactive streams and components on OSGi

Reactive frameworks allow to implement non blocking asynchronous processing in a convenient way. This talk explores how to apply reactive patterns to typical use cases on OSGi like REST services, MQTT Message processing, computations over sliding windows.

We combine messaging and streams like also used in Spring 5 to create services that are highly scalable while not tying the user to the technologies being used.

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