To be clear - I'm not saying that analytics and data engineering are a fad. I'm not saying the data teams are doomed to fade away, or that the old fundamentals of data modeling are wrong, or that the urge to quantify everything is a mistake. I'm saying that things seem pretty good, right now. But, you know. Like Charles Schwab constantly says, past performance is no guarantee of future results. So someone else might say all of that in the future - because, as John Maynard Keynes said, in the long run, we are all dead.

For years, data engineering was a story of predictable "pipelines": move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs. This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

In this talk the somewhat biased Apache Spark PMC Holden will explore the times when using Spark is more likely to lead to disappointment and pages than success and promotions. We'll, of course, look at places where Spark can excel but also explore heuristics like if it fits in Excel double check if you need Spark. By using Spark only when it's truly beneficial you can demonstrate that elusive "thought leadership" that always seems to be required for the next level of promotion. We'll explore how some of Spark's largest disadvantages are changing, but also which ones are likely to stick around -- allowing you to seem like you have a magic tech eightball next time someone asks you to design your analytics strategy. Come for a place to sit after lunch and stay for the OOM therapy.

Start with a dataset in Motherduck and build a production-ready analytics app using Omni’s semantic model and APIs. We’ll cover practical data modeling techniques, share lessons learned from building AI features, and walk through how to give AI the context it needs to answer questions accurately. You’ll leave with a working app and the skills to build your next one.

Everyone's trying to make LLMs "accurate." But the real challenge isn't accuracy — it's context. We'll explore why traditional approaches like evals suites or synthetic question sets fall short, and how successful AI systems are built instead through compounding context over time. Hex enables a new workflow for conversational analytics that grows smarter with every interaction. With Hex's Notebook Agent and Threads, business users define the questions that matter while data teams refine, audit, and operationalize them into durable, trusted workflows. In this model, "tests" aren't written in isolation by data teams — they're defined by the business and operationalized through data workflows. The result is a living system of context — not a static set of prompts or tests — that evolves alongside your organization. Join us for a candid discussion on what's working in production AI systems, and get hands-on building context-aware analytical workflows in Hex!