Databricks

Brickster Voices is a series that spotlights the people who make our work possible. Through personal career journeys, behind-the-scenes looks at impactful projects and a glimpse into how we work together, these stories offer a window into life at Databricks. Whether you're exploring new opportunities, curious about our work in data and AI or simply inspired by stories of growth and collaboration, Brickster Voices invites you to get to know the individuals driving our mission forward.

For Flo Health, the bar for generative AI is unusually high. With 81 million monthly active users, the women’s health app operates at a scale where even small weaknesses in an AI system matter. Because Flo’s products deal with health and well-being, responses need to be medically safe and genuinely useful. As Flo moved from using only traditional ML towards personalized insights powered by generative AI, the team saw the potential for a more flexible, personalized product.

TinyFish builds infrastructure for enterprises that need agents to work across the live web. Its Search, Fetch, Browser, and Agent APIs support large-scale, authenticated web workflows, where an agent may need to navigate dynamic sites, retrieve information, interact with applications, and carry out a task across multiple steps. TinyFish is developing its own specialized web-native model for these agent tasks.

The California Department of Health Care Services (DHCS) built the Behavioral Health Public County Profile on the Databricks platform, a first-of-its-kind public dashboard that brings behavioral health data from all 58 counties into one place for nearly 40 million Californians.

When every tier of the defense supply chain can share governed data with the people who need it, the result is a real-time operating picture that connects cost evidence, supplier status, and production commitments to program and portfolio decisions. That picture does not exist today, but the technology to enable it does. Why the DoW can't see what its supply chain actually costs The Department of War (DoW) cannot see its own supply chain.

Richard Tomlinson, who leads product marketing for Databricks' business intelligence and analytics products, has spent his career championing technology that tames data to help people make better decisions and fuel innovation. In this conversation, he discusses how natural-language querying for visualizations can finally offer more than SQL, how to make sure AI-powered analytics are trustworthy and how business context is key.

Operational databases like Postgres are built to reliably execute highly concurrent, low-latency queries across subsets of data. What often impacts this reliability are bulk operations, like loading terabytes of data or running analytical queries that scan an entire table. These operations compete for the same resources as your application workloads, risking performance degradation or downtime. Our goal is to make Lakebase Postgres the safest, most reliable place for your operational workloads.

AI has changed how software gets built. As coding agents take on a growing share of development work, developers are increasingly shifting toward orchestrating them. Running multiple agents in parallel is becoming the norm, and the tooling has evolved with it, from skills and hooks to MCPs, subagents, all designed to make agents safer and more effective. Yet, one mission-critical part in the development workflow is still often overlooked: the database.

The HL7v2 problem in the lakehouse HL7v2 is the messaging standard that quietly runs healthcare. It’s how clinical systems tell each other that a patient was admitted, that a lab order was placed, or that a result came back. Decades after it was introduced, it remains the most widely deployed integration standard in the industry, and the vast majority of care organizations still depend on it for the routine flow of operational data. It is also hard to work with.

Part I — The problem Four sectors, one bottleneck Hospitals and health systems generate the raw material. Radiology is the most heavily regulated corner of medical AI anywhere: of the roughly 950 AI/ML-enabled devices the FDA had authorized by mid-2024, about 723 (around 76%) were radiology tools. Almost all are narrow and human-supervised, doing triage, measurement, reconstruction, or worklist prioritization. The real difficulty in a hospital is rarely the model.