Join us for a meetup on June 4th in our Elastic office in Amsterdam, together with the Amsterdam JUG User Group! Doors open at 17.45 and the presentations begin at 18.00. Food, refreshments, and networking to follow. We wrap up around 20.00.

We are rating our talks as follows:
🟢 = Beginner content
🟡 = Intermediate content
🔵 = Expert content

Address: Elastic's office, Keizersgracht 281, 1016 ED Amsterdam

Agenda:
17.45 Doors open
18.00 One Does Not Simply Query a Stream
18.45 ES|QL FTW!
19.30 Networking, food with drinks
20.00 Wrap up

Talks:
One Does Not Simply Query a Stream
🟢🟡 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 stream processing with Kafka Streams, Apache Flink®, and SQL engines; real-time analytics with Apache Pinot® and ClickHouse; search capabilities with Elasticsearch; and 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.

Speaker: Viktor Gamov, Principal Developer Advocate, Confluent

ES|QL FTW!
🟡 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.

Speaker: Piotr Przybyl, Senior Developer Advocate, Elastic