Article by David Allen. Focused on Graph processing for Kafka Stream visualizations. Apache Kafka® is great when one needs to dealing with streams, allowing you to conveniently look at streams as tables. Stream processing engines like KSQL furthermore give you the ability to manipulate all of this fluently.
Graphs are a more natural representation with a lot of grounding in theory for some use cases. So we can improve a portion of just about any event streaming application by adding graph abilities to it.
An article explains how to use graphs on social network example:
- A stream of friend relationships
- Graph the network with Neo4j
- Configuring Neo4j to interact with Kafka
- Using graph algorithms to recommend potential friends
- Link prediction algorithms
- Common Neighbors algorithm
- Publishing back to Kafka
All of the code and setup discussed in this blog post can be found in this GitHub repository, so you can try it yourself! Charts and all the data are well explained. Nice one!
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