Elastic Meetup x Apache Kafka® Cologne Meetup

You're welcome back to the Elastic RheinRhur meetup taking place on October 29th in collaboration with the Köln Apache Kafka® Meetup Group by Confluent! Doors open at 18.15 with pizza, networking and drinks followed by presentations at 18.45.

*PLEASE ONLY REGISTER ONCE FOR THIS EVENT - YOU DO NOT NEED TO REGISTER ON KÖLN APACHE KAFKA® IF YOU ARE REGISTERED HERE *

Address: lifelab | Workshop & Event Location
Ebertpl. 23, 50668, Köln

Please note: you must provide your full name (first + last) when signing up for security access.

18.15: Doors open, join us for pizzas and a drink
18.45: Talk #1 - ES|QL
19.30: Talk #2 - Taming the Cost of Kafka Workloads In the Cloud
20.15: Networking
20.30: Close

Talks:
Elasticsearch Query Language (ES|QL)
Elasticsearch and Kibana added a new query language: ES|QL — coming with a new endpoint (_query) and a brand new syntax. It let's you refine your results one step at a time and adds new features like data enrichment and processing right in your query. And you can use it across the Elastic Stack — from the Elasticsearch API to Discover and Alerting in Kibana. But the biggest change is behind the scene: Using a new compute engine that was built with performance in mind.
Join us for a quick overview and look at syntax and internals.
Speaker: Antje Baranek (Senior Solutions Architect)

Taming the Cost of Kafka Workloads In the Cloud
Cloud computing has changed how we operate and think about software: At any time, we have instant access to a seemingly unlimited capacity of compute resources and can scale our applications elastically on demand. We no longer have to procure physical machines and pay for them upfront but are billed for only those resources that we have actually used. While the usage-based pricing sounds great at first glance, it makes cost planning a complicated task, often leading to huge surprises when receiving invoices for cloud services, as revealed in numerous blog posts of authors waking up to invoice amounts that are multiple orders of magnitude higher than expected.
This talk explores the main cost factors of Kafka workloads in the cloud: compute, network, and storage. We identify their contribution to the overall cost and walk through different techniques for reducing their cost footprint by, for instance, using Kafka's follower fetching to reduce cross-AZ traffic, compression to reduce overall traffic, or scaling streaming applications ""to zero"" in the absence of incoming events to save compute resources. We focus on setups where both Kafka and associated workloads, such as Kafka Streams or Flink applications, are operated on cloud platforms.
Speaker: Stefan Sprenger (Staff Software Developer I @ Confluent)

We look forward to seeing you there!