The Anti-Hype Conference for Open Source Analytics and AI

You must complete registration here: https://luma.com/eis7567t. We communicate ALL event communications with registrants there.

If you plan to attend in person, you must do an additional sign up on AWS Builder Loft website: https://events.builder.aws.com/ReRbol. This is for security reasons.

Description

​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