OSA Con 2026 - The Anti-Hype Conference for Open Source Analytics and AI
For in-person attendees: Because this event takes place in the AWS builders loft, please also register HERE. ID REQUIRED FOR ENTRY!
2026 is the year agentic AI finally works, and it’s changing open source analytics.
OSA Con 2026 brings together engineers, architects, and builders working at the intersection of open source data infrastructure and AI. No vendor pitches or 10x AI miracles. Just deep technical talks from great engineers building stuff that works.
We’ll get into AI workloads and data management, agents building and operating analytic platforms, and analytics powering autonomous actions, alongside what’s happening in real-time analytics, shared data lakes, and emerging open-source architectures.
📍 Nov 2 · AWS Builder Loft, San Francisco + Online · [Free to attend / Limited in-person capacity]
Speakers
See their abstracts at osacon.io
Lisa Cao — Databricks: Getting AI agents to write better Apache Spark pipelines
Wei-Chin Call — Grafana Labs: Benchmarking AI agents that debug your dashboards
Jason “Jay” Smith — Google: Serverless eventing for simpler, scalable AI data pipelines
Aditi Pandit — IBM: Inside the Presto C++ engine: production experience, performance & the 2026 roadmap
Alex Merced — Dremio: Building the Open Agentic Lakehouse for data + AI
Heather Meeker, Roman Shaposhnik (Panel discussion): Whose Code Is It Anyway? AI-generated code, ownership & open-source licensing
David Morrison — Applied Computing Research Labs: 10 infrastructure dashboards you can’t build with Grafana
Patrick McFadin — McFadin Data & AI Advisory: Why we’re still paying rent on our own data—and where lock-in is moving
Felicitas Pojtinger — Loophole Labs: Building legacy-free RISC-V Kubernetes clusters with ClickHouse®
Matthew Topol — Columnar / Apache Software Foundation: What it really takes to run ADBC in production
Brandon Wilcox — Gamebeast: Building AI-powered LiveOps and analytics for gaming
MORE COMING SOON
What to Expect
Deep technical sessions from engineers building and running analytics platforms in production
AI agents, model evaluation, real-time analytics, data lakes, and modern open-source infrastructure
Architecture, performance, scalability, reliability, and cost—without the vendor pitches
Meet engineers, architects, maintainers, and open-source contributors building the next generation of data systems
Who Should Attend
Data engineers and analytics engineers
Platform engineers working on data infrastructure
Architects and technical decision-makers designing analytics systems
Anyone interested in the intersection of analytics, AI, and modern data platforms