Data Breakfast – Kill Your ETL: One Platform for Transactions, Analytics, and AI
MeetupIn person
Grev Wedels Plass 9, OSLO, Norway
Most data organisations run complex platforms with three to five separate databases and a lot of data movement: transactional database, data warehouse, streaming layer, vector store, maybe a feature store for AI. Each with its own budget, its own team, and its own renewal negotiation. The conventional wisdom is that this is necessary. The technology has moved past that assumption, and the economics have moved with it. Compound this with Sovereignty concerns and it becomes difficult to see the simple solution.
This is a 90-minute breakfast session for data leaders who want to understand what's changed, what's now easy, and what it means for their next platform decision.
What you'll see
A live demo of a single open-source platform handling transactional, analytical, and AI workloads on the same dataset. No data duplication. No ETL pipelines. Strict ACID consistency across the entire platform. Built entirely from Apache-licensed open-source components.
Cost comparison
How this approach runs 80%+ cheaper at petabyte scale than a Postgres + ETL + Snowflake or Databricks stack, with the cost model documented and auditable. Every enterprise has different architectural needs and we'll demonstrate how to model costs comparisons against your needs.
What you'll take away
A clear picture of what open-source data infrastructure can do in 2026 that it couldn't three years ago, and a framework for evaluating whether your current platform architecture is the right long-term bet, or whether you're paying a complexity tax that's no longer necessary. Whether you're committed to Snowflake, Databricks, or BigQuery, this session gives you the context to pressure-test that commitment with real numbers.
Who this is for
CDOs and Heads of Data evaluating platform consolidation.
CTOs assessing long-term vendor risk.
Solution architects and data platform leads who want the technical reality before recommending a direction.
If you are responsible for a data-platform budget and wonderin