IN-PERSON: Apache Kafka® Meetup

Join us for an Apache Kafka® meetup on Thursday, May 7th from 6:00pm at matecoIT and hosted by Cymo!

📍Venue:
matecoIT
Mannebeekstraat 4 8790 Waregem

🗓 Agenda:

• 5:30pm: Doors open
• 6:00pm - 6:30pm: Yennick Trevels, Full-stack Data Engineer @ DigitalBuff
• 6:30pm - 7:00pm: Andreas Evers, CTO @ KOR
• 7:00pm - 7:30pm: Geert Pante, Owner Hi10 & Product Engineer @ matecoIT
• 7:30pm - 8:30pm: Additional Q&A & Networking

💡Speaker One:
Yennick Trevels, Full-stack Data Engineer, DigitalBuff

Title of Talk:
How Things Work - Inside Kafka Streams

Abstract:
In this session, we’ll dissect Kafka Streams, one of the major streaming frameworks within the Kafka ecosystem. We’ll uncover how it achieves high performance, high availability, and scalability — characteristics found in many distributed systems. You’ll come away with reusable patterns that apply across distributed systems, a sharper lens for comparing frameworks, and a clearer view of the strengths and weaknesses of a streaming framework.

💡 Speaker Two:
Andreas Evers, CTO @ KOR

Title of Talk:
Kafka Streams Pushed Hard: Lessons from Stream Processing at Scale

Abstract:
Kafka Streams is a powerful choice for stateful stream processing: a library, not a cluster, that runs alongside your application and handles exactly-once semantics, fault tolerance, and partition-local state out of the box. At KOR, we build financial-grade trade reporting infrastructure on top of it. That means regulatory deadlines, SLA requirements, and zero tolerance for data loss. We pushed Kafka Streams hard.
This talk is about what we found.
We will walk through four walls we hit in production. Not theoretical limitations, but real engineering decisions with real consequences. First, what happens when your access patterns outgrow key-value stores, and why plugging in a custom document database is a much larger commitment than the API suggests. Second, why Interactive Queries can lead you to accidentally build a distributed database inside your application. Third, why making external calls from within a stream processor will eventually crash your application, and the request-response offloading pattern with a parking lot that solves it. Fourth, the partition ceiling, the hard limit on Kafka Streams parallelism, what your options are when you hit it, and why we studied Flink seriously but chose not to run it.
Each section covers a constraint, the wall it created, and the exit we found or considered. Some exits are clean. Some are costly. All of them are honest.
If you are running Kafka Streams in production, or planning to, this talk will save you some expensive discoveries.

Bio:

💡 Speaker Three:
Geert Pante, Owner Hi10 & Product Engineer @ matecoIT

Title of Talk:
Do’s and Don’ts of Kafka Streams

Abstract:
Kafka Streams promises a rich toolbox: declarative processing, distributed Interactive Queries, and a clean developer experience.

Yet the moment you dive in head‑first, you quickly discover there are patterns to embrace, ... and pitfalls to avoid.

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DISCLAIMER
We are unable to cater for any attendees under the age of 18.
If you would like to speak or host our next event please let us know! community@confluent.io