Enterprises are racing to integrate AI into their workflows, but success depends on more than just powerful models. Databricks and Azure OpenAI are delivering secure, governed methods to connect and ...

The IPO window may have cracked open, but it seems some former startups have no intention of going public. Makes sense, in a way: IPOs were traditionally a way to raise money, and if you can manage to ...

Automation and integration were key themes at Databricks’ annual customer conference, as it showed off new generative and agentic AI features coming to its cloud data lakehouse platform. At Databricks ...

Databricks, known for secure data storage and AI, has launched a new AI business intelligence dashboard called Databricks One, designed for nontechnical business users across various departments.

Databricks has expanded its integration with Microsoft’s ecosystem, introducing a Power Platform connector and new Azure Databricks capabilities to link AI, analytics, and collaboration tools. These ...

Databricks is introducing a security information and event management service called Lakewatch. The privately held company sees an opportunity to challenge mature cybersecurity vendors using ...

Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving engines of insight. In the fast-evolving landscape of enterprise data ...

Databricks has attracted increasing attention in recent months. Although it is currently a privately held company, it raised a considerable amount of money earlier this year and reported an annualized ...

Founders: Ali Ghodsi (CEO), Matei Zaharia, Reynold Xin, Ion Stoica, Patrick Wendell, Andy Konwinski, Arsalan Tavakoli-Shiraji Launched: 2013 Headquarters: San Francisco Funding: $19 billion Valuation: ...

Over the past five years, advances in AI models’ data processing and reasoning capabilities have driven enterprise and industrial developers to pursue larger models and more ambitious benchmarks. Now, ...

CISOs know precisely where their AI nightmare unfolds fastest. It's inference, the vulnerable stage where live models meet real-world data, leaving enterprises exposed to prompt injection, data leaks, ...