Maria Zervou

Genie Code adds tremendous value for data engineers - unifying and automating much of the ingest, ETL and streaming process. Hear from Bilal Aslam (Senior Director of Product Management) and our customer SEGA on how to use Genie Code for agentic data engineering, automating much of the process of building intelligent data pipelines. It integrates into the broader Databricks environment, and with Lakeflow and Spark Declarative Pipelines is a fully governed way to prepare and analyze data directly in Databricks.

AI-driven attacks now operate at machine scale, with mean time to exploit collapsing from 23 days to just under 2 days. SIEMs as we know them today are going to disappear because this new agentic era you are dealing with unprecedented threats. That’s why Databricks announced Lakewatch, the Open Agentic SIEM. Instead of locking security data into proprietary systems with per-byte ingest licensing, Lakewatch lets teams store all their security telemetry in open formats, retain it for years, and run agentic AI across it. Security teams gain complete visibility across the enterprise and can deploy defensive security agents to automate threat detection and response at massive scale.

Most AI failures aren't technology problems — they're people, process, and architecture problems. In this hands-on session, you'll witness a live AI trainwreck: an agent operating without guardrails, governance, or the right organizational design. Then, in a real-time post-mortem, we will collectively dissect what broke and why. You'll map the missing middle layer: the intersection of roles, accountability structures, and platform architecture. And you'll work alongside peers to design practical fixes. The session closes with an exercise where attendees act as domain experts to train an AI Judge capable of scaling oversight across enterprise deployments. Expect minimal slides, maximum doing, and a concrete blueprint you can apply within 30 days. This session is built for executives who are tired of theoretical AI frameworks and want proven patterns that make AI projects safer, faster, and genuinely business-ready.

Lakebase radically simplifies how companies manage their transactional data, seamlessly integrating with your Lakehouse. It's a fully managed Postgres OLTP data base engine that's built for AI-native development and vibe coding workflows. All with lower TCO, less operational overhead, and is open-source so no lock-in. Hear from Bryan Clark, who leads the Lakebase product, on Databricks strategy, where the market is headed, and how this benefits AI apps and agents.

You'll hear a lot of success stories this week, but it also pays to learn from mistakes. Hear from three experts on what they've seen over the last few years that guarantee a project will fail. Robin Sutara is an expert in enterprise wide AI adoption via people and process, Maria is a lead advisor in AI project success and Holly spends her days talking to practitioners. Together they've seen a broad spectrum on the best and the worst that AI has to offer.

Lakebase is a new approach to radically simplify how organizations operate. It's a new category of OLTP databases based on Postgres that allows users to seamlessly integrate real-time OLTP workloads with the Lakehouse. Hear from Andre Landgraf, a key voice of the Lakebase community on the technology behind it all, and how to get started building.

Hear about the latest Lakehouse RT and Reyden announcements. Shant, Distinguished Engineer at Databricks, gives an in-depth discussion of achieving millisecond performance at massive scale - directly on your lake. Also Pramod Sadalage from Thoughtworks will add his thoughts on the impact of Lakebase on software development.

Databricks Unity Catalog is a unified governance layer that centralizes access control, auditing, lineage, and data discovery for all data and AI assets across multiple workspaces and clouds. Hear from Raj Gossain on everything Unity Catalog including what's new such as semantics with learned ontology from automated processes.

Viktoria Semaan walks you through two ways to build data apps on Databricks, using a real-world use case: extracting insights from public company 10-K filings, storing structured financial data in Databricks, and running analytics on it immediately. The first path uses Claude Code with the Databricks AI Dev Kit, the production-grade approach for enterprise developers who want full control, governance, and deep Databricks integration. The second path uses Lovable, the natural fit for vibe coding, rapid prototyping, and getting non-developers building fast. Both connect directly to Databricks. You will leave with a clear view of the trade-offs between the two approaches, the resources to get started the same day, and a new benchmark for how fast intelligent data apps can be built and deployed.