Amsterdam JUG Meetup with Elastic Netherlands User Group

Join in with the latest Amsterdam JUG Meetup at Elastic, Keizersgracht 281 · Amsterdam, NH.

Note: This meetup is being held together with the Elactic Netherlands User Group (making available a limit of 50 attendees each), who also have a registration page for this event, so if you have already registered at their meetup page (here), there's no need to register again here, i.e., don't register here if you're already registered there.

For more details and discussions on the below, go to bit.ly/join-foojay-slack to join the Friends of OpenJDK (Foojay.io) Slack and use the #jug-amsterdam channel for conversations related to the below.

In this meetup, in addition to food, drinks, and networking, you will experience the following program and talks:

17.45: Doors open
18.00: "One Does Not Simply Query a Stream"—Viktor Gamov, Principal Developer Advocate, Confluent
18.45: "ES|QL FTW!"—Piotr Przybyl, Senior Developer Advocate, Elastic
19.30: Networking, food with drinks
20.00: Wrap up

Abstracts

"One Does Not Simply Query a Stream"—Viktor Gamov, Principal Developer Advocate, Confluent

Streaming data with Apache Kafka has become the backbone of modern applications. While streams are ideal for continuous data flow, they lack built-in querying capabilities. Unlike databases with indexed lookups, Kafka’s append-only logs are designed for high-throughput processing—not for on-demand queries. This necessitates additional infrastructure to query streaming data effectively.

Traditional approaches replicate stream data into external stores: relational databases like PostgreSQL for operational queries, object storage like S3 accessed via Flink, Spark, or Trino for analytics, and Elasticsearch for full-text search and log analytics. Each serves a purpose—but they also introduce silos, schema mismatches, freshness issues, and complex ETL pipelines that increase system fragility.

In this session, we’ll explore solutions that aim to unify operational, analytical, and search workloads across real-time data. We'll demonstrate the following:

• stream processing with Kafka Streams, Apache Flink, and SQL engines
• real-time analytics with Apache Pinot and ClickHouse
• search capabilities with Elasticsearch
• modern lakehouse approaches using Apache Iceberg with Tableflow to represent Kafka topics as queryable tables

While there's no one-size-fits-all solution, understanding the tools and trade-offs will help you design more robust and flexible architectures.

"ES|QL FTW!"—Piotr Przybyl, Senior Developer Advocate, Elastic

NoSQL for years was associated with JSON. The thing is: if you're a hardcore backend Java developer, JSON, YAML, and other data formats might not feel native to you. Also, if you were ears-deep into debugging a query from Java code, sending the same request for visualization in Kibana using KQL wasn't trivial.

Meet ES|QL: Elasticsearch's new query language, being at first glance a mixture of SQL and... Bash ;-) Works the same in Java and Kibana (and other programming languages too!) Additionally, by leveraging Project Valhalla and vector operations, ES|QL can achieve performance improvements over previous solutions.

If you're eager to investigate the options of the ES|QL and how it makes your life easier (while also giving a feel of being a SQL DB), this talk is for you.