Open Lakehouse and AI - Austin

**IMPORTANT: Please Register on Luma to Attend: https://luma.com/2cxlufyd**

​The Open Source Analytics Community is excited to bring an evening of insights and networking to Austin.

​Real-time databases are converging with data lakes to lower storage costs, improve performance, and make data more accessible for AI and data science. Our speakers will share challenges, solutions, and real-world experiences as organizations move from closed storage systems to open formats like Apache Iceberg.

# ​Speakers

• ​​Robert Hodges, CEO @ Altinity
• ​Steve Anness, Senior Customer Success Architect @ Grafana
• ​Team member @ CelerData

# ​Agenda

• ​6:00 pm - Check-in and networking
• ​6:15 - 8:00 pm - Talks
• ​8:00 - 9:00 pm - Networking

# ​​​Description of the Talks

## ​​​Building a Foundation for AI with ClickHouse® and Apache Iceberg Storage

​​​Speaker: Robert Hodges, CEO @ Altinity
​​​Abstract: AI applications need data. Lots of it. Altinity's Project Antalya is adapting open source ClickHouse® to introduce separation of compute and storage on shared Iceberg table data. The result: fast, cheap, flexible query that extends the life of real-time analytic applications and lays the foundation for handling new AI use cases on the same datasets. We cover architecture, performance results, roadmap, and how to get started yourself.

## ​​

## ​​​Visualizing Your Data Lake with Grafana

​​​Speaker: Steve Anness, Senior Customer Success Architect @ Grafana
​​​Abstract: In this brief talk, we’ll walk through how to get started with Grafana’s open source platform to explore and understand your data lake. We’ll cover how to connect to your data—no matter where it lives—then craft queries that turn raw information into clear, compelling visualizations, and finally set up alerts and annotations so you’re always in the know when something important changes in your data lake.
​

## ​Achieving High-Performance Analytics on Apache Iceberg

​​Speaker: Team member @ CelerData
​​Abstract: Apache Iceberg enables open and flexible lakehouse architectures, yet delivering low-latency, high-concurrency analytics in production remains a significant challenge. This talk examines the key factors that constrain query performance on Iceberg and explores how modern query engines, such as StarRocks, address these challenges.
​​We’ll examine common bottlenecks such as metadata overhead, delete handling (position and equality deletes), and query planning costs under concurrency. The session then dives into practical optimization techniques—including scalable metadata parsing, engine-level execution optimizations, and best practices for production Iceberg workloads.
​​Backed by real-world enterprise use cases, this talk provides actionable insights for engineers looking to run fast, reliable analytics on Apache Iceberg at scale.