Capturing CAP in a Kappa Data Architecture


When choosing different architectures for your enterprise data environment, you always want to make sure how to manage choices in the CAP theorem. The CAP theorem states that you can not have consistency, availability, and partition-tolerance at the same time, but what if choosing for Kappa architecture makes it possible to have it all?

To make sure you make the right choice, you have to know what Kappa is, how it compares to Lambda, microservice and monolith architectures. After a quick refresher on architecture, we're going forward by showing a practical way on how to setup a Kappa architecture by using Kafka, the de facto standard for event storing and stream processing. But is Kafka the right choice? Lets find out by comparing live:

Setting up Kappa architecture with Kafka running in Kubernetes
Using it with commercial distributions of Kafka(Confluent and/or Axual)
Using an alternative kafka: Red panda
Using an alternative non-kafka: Redis(trust me, this will make sense)

And at the end of the demo we know: if Kafka is the best Kafka, what the right choice for your Kappa architecture is and if you actually can beat the CAP theorem with.


Joep has more than 12 years experience of developing, engineering, architecting and visualising data products in various markets ranging from energy to clothing manufacturing. He’s focussing on enabling teams to be better at handling data and providing the teams with the tools and the knowledge needed to go live and to stay in production.

He was member of the Teqnation program committee), did a presentation on Kafka and Hue usage during football, developing and deploying on Hololens, Total Devops using Gitlab, Evolution of a datascience product, Using the elastic stack from PoC to Production, Xbox Kinect on a bike at Devoxx London

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