Our CEO, Robert Hodges, is presenting the talk What’s a Data Lake and What Does It Mean For My Open Source Stack? at the SeaGL 2025 Conference. The 2025 conference will be held November 7-8.
Data lakes on open table formats like Iceberg are a popular way to manage large datasets for analytics, data science, and AI. This talk explains how data lakes work and how to adapt open-source analytic stacks to use them. First, we'll tour projects like Arrow, Iceberg, and Unity Catalog that make data lakes possible. Next, we'll see how analytics engines like DuckDB, ClickHouse, and Spark are adapting. Finally, we'll survey a few projects that enable applications written in Python, Golang, or Rust to deliver fast queries. You'll have to build the app yourself, but this talk will show you a path to use data lakes and open source successfully.

Joshua Lee, is presenting the talk Magical Mystery Tour: A Roundup of Observability Datastores at the SeaGL 2025 Conference. In this talk, Joshua will guide attendees through the foundational principles of telemetry—covering metrics, traces, logs, profiles, and wide events—and break down the strengths and limitations of different database technologies for each use case.
By the end of this session, attendees will have a clearer understanding of the trade-offs between these datastores and how to make informed decisions based on the unique requirements of their systems. Whether you’re building an observability stack from scratch or looking to optimize an existing setup, this tour of the observability datastore landscape will leave you better equipped to navigate the options.

Our CEO, Robert Hodges, is presenting the talk What’s a Data Lake and What Does It Mean For My Open Source Stack? at the Data CON LA 2025 Conference. The 2025 conference will be held on November 8.
Data lakes on open table formats are emerging as the go-to storage of large datasets for analytics, data science, and AI. This talk explains how data lakes work and how ClickHouse® is integrating them.
We’ll introduce the key components of data lakes using a concrete example based on Parquet, Iceberg open table format, and the Iceberg REST catalog. Next, we’ll look at new ClickHouse® feature adaptations, exploring specific issues like event stream ingest, compaction, and queries.
Finally, we’ll illustrate how to combine ClickHouse® with Apache Spark and Kafka to deliver fast analytics on massive, shared tables. The real-time data lake is arriving, and ClickHouse® is going to be a big part of it.