Databricks

Unity Catalog was designed for interoperability at scale. Enterprises have the flexibility to run any engine on a single copy of data with governance enforced centrally. Today, we are taking interoperability a step further. We're excited to announce that external access to Unity Catalog (UC) managed Delta tables is now in Public Preview.

In a perfect world, ML Features are built only once. But for many teams, a feature that works in a notebook still turns into duplicated logic, fragile pipelines, one-off backfills, online store plumbing, and governance overhead. For real-time use cases like fraud detection, personalization, and recommendations, that complexity gets even harder to absorb because models depend on fresh signals to make accurate predictions.

There's a version of the AI modernization story that goes: build the platform, then figure out the use cases. Ankur Jain would tell you that's backwards — and that most organizations are learning that the hard way.

How Databricks hackathons work We run these hackathons for a simple reason: the fastest way to learn a product is to build something with it. Each one kicks off with an enablement session where teams get to know the product and where it is headed on the roadmap. Then the building starts. Teams work together for about a week and submit a project, and the strongest solutions earn prizes. This was our fifth, and the product in the spotlight was Databricks Genie.

Ambulatory care, the outpatient clinics and physician practices where most patients interact with a health system, is where growth is won or lost. Yet many health systems struggle with the operational constraints that limit access to care: provider capacity, referral retention, and downstream financial performance. Access is moving in the wrong direction.

Most enterprise analytics evaluations are really just dashboard comparisons. That's the wrong starting point. The question that matters isn't which vendor has the best UI — it's whether analytics, AI and agents all run on the same data. One is a product decision. The other is an architecture decision that will shape what your data team can build for the next decade.

Azure Synapse has served as a reliable foundation for SQL analytics at scale, and teams that built on it made a sensible choice at the time. However, a platform primarily designed around a data warehouse isn't built for the full range of what data teams are now expected to deliver. Filling those gaps usually involves adding more services, integrations and operational overhead, which builds up over time. Migrating to Databricks is one way to address this issue.

At Databricks, the way we build software is changing quickly as we aggressively adopt AI for engineering. The landscape of models and harnesses for code authoring has rapidly expanded in the last year, giving developers more choices than ever. With more options, it has become increasingly important to understand which coding agents offer the best performance on real-world coding tasks as well as understanding how task-performance varies with price.

Imagine being unable to tell your doctor whether you're in pain or running a fever. This is a reality for many people living with dementia — and it means doctors can struggle to make the right diagnosis, leading to delayed treatment. For people living with dementia, subtle changes such as sleep disruption, reduced movement and shifts in daily routine can signal meaningful changes in health.

When a security analyst is alerted to a suspicious login or an unusual firewall block, the answer is almost always in the logs. Finding it is the hard part. Barracuda Managed XDR is an AI-powered security service that combines advanced detection with a dedicated Security Operations Center (SOC) to help organizations protect against, detect, and respond to threats across endpoints, networks, email, and cloud environments.