Doug MacWilliams

Your lakehouse handles analytics beautifully. But the moment someone needs low-latency reads, transactional writes or an app serving layer, you're stitching together external databases and sync pipelines. Lakebase offers a different path — this session shows you how to build on it. A Databricks MVP will walk through practical patterns for building production apps where Lakebase serves as the operational layer, fully integrated with Unity Catalog governance and Lakehouse analytics. We'll cover architecture patterns, the interplay between Lakebase and Databricks Apps, latency and scaling considerations, and the honest gaps practitioners need to plan around. You'll leave with architecture patterns, design decisions and a realistic assessment of production readiness.

Seismic shift Large Language Models are unleashing on data engineering, challenging traditional workflows. LLMs obliterate inefficiencies and redefine productivity. AI powerhouses automate complex tasks like documentation, code translation, and data model development with unprecedented speed and precision. Integrating LLMs into tools promises to reduce offshore dependency, fostering agile onshore innovation. Harnessing LLMs' full potential involves challenges, requiring deep dives into domain-specific data and strategic business alignment. Session will addresses deploying LLMs effectively, overcoming data management hurdles, and fostering collaboration between engineers and stakeholders. Join us to explore a future where LLMs redefine possibilities, inviting you to embrace AI-driven innovation and position your organization as a leader in data engineering.