Genie lets business users ask questions of their governed Databricks data in plain language and get answers backed by SQL run against the real data - no query-writing, and no access to data they couldn’t already see. The rest of this article covers what it does, why the answers hold up, and how to set one up well.

Databricks Unity AI Gateway is the governance layer that lets enterprises control cost, security, and model choice across the AI models, agents, MCP services, and skills they govern through Unity Catalog, from a single control plane. It became generally available on August 4, 2026 , after being announced in expanded form at Data + AI Summit 2026 .

Learn how Databricks Genie Ontology uses OntoRank, Unity Catalog and organizational knowledge to create a reusable context layer for Genie Agents, Genie One and enterprise AI applications.

Databricks Data + AI Summit 2026 introduced one of the largest sets of platform updates in the company’s history. While many announcements focused on new products and features, a broader pattern emerged across the keynote sessions and technical tracks.

Global supply chains have never been under more strain, and the systems meant to manage them have never looked more dated. Most planning and logistics organizations still run on a patchwork: shipment tracking in one tool, production planning in an ERP, risk analysis in a spreadsheet, and forecasting that is often manual or missing entirely. Each system is reasonable on its own. Together, they leave the business without a single, trustworthy picture of what is actually happening.

How to build an end-to-end hyper-personalisation solution that combines machine learning, generative AI, real-time event processing, journey orchestration, and adversarial AI safeguards — all on a single platform.

McKinsey has found that for food companies, every 1% improvement in direct material cost can improve net margin by 200 to 300 basis points. That makes ingredient procurement one of the most powerful margin levers in the industry. Yet many food and beverage manufacturers still find it difficult to fully capture this value.

We are proud to share that Datapao has been named to the Financial Times FT1000 – Europe’s Fastest Growing Companies 2026 ranking — marking our third consecutive year on the list.

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When we ski down a perfectly groomed slope in the early morning, we rarely think about the streams of data pulsing behind the scenes.

If you’ve worked in enterprise AI for long enough, you know this story: A team builds a generative AI proof of concept. It looks promising. Everyone’s excited. And then the pilot quietly disappears, not because it failed, but because scaling it responsibly turned out to be too complex, too risky, or too expensive.

In today’s AI-driven world, enterprises generate and process more data than ever before, often spread across multiple clouds, teams, environments. Managing who can access what, tracking how data moves, and keeping everything compliant has become a real challenge.

From complex data to clear, trustworthy insights — powered by deep agentic AI on Databricks

The recent announcement that the London Stock Exchange Group (LSEG) will make its datasets available natively in Databricks through Delta Sharing has generated excitement across financial services. For good reason: market data has long been locked behind legacy delivery systems, and moving it directly into the Databricks environment removes significant friction.

With the public preview release of Databricks Lakebase , we explored its practical applications in a real-world generative AI project. This article walks you through how we integrated Lakebase into our agent orchestration platform , focusing on usage patterns, performance gains, and cutting costs.

Over the past few years, the tech industry has experienced waves of uncertainty. From hiring booms to high-profile layoffs, software engineers have had to rethink not just where they work, but where their skills can take them next.

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Understand how 600 tech leaders think about AI and find out how to future-proof your business before adopting the emerging tech.

Discover three real-life case studies of companies DATAPAO has helped right here.

Cloud data infrastructure can boost innovation and optimize operations for any company. But how do you migrate smartly?

Which is better for your migration project? Find out right here!

Discover exactly how migrating to the cloud can improve a company and help it grow.

Understanding the two most popular cloud-based data platforms helps to make an informed decision.

We break down the stages of migrating your data infrastructure to the cloud, step by step.

Find out how DATAPAO helped a fintech realize its data vision with the right architecture.

Discover the 5 cloud data migration strategies & pros and cons for each.