Databricks Inc.’s claim to fame is the tactful combination of data lakes and data warehouses into what it calls the “data lakehouse architecture.”

Artificial intelligence has emerged as the universal enabler. Seeing that, Databricks is empowering its users by seamlessly unifying the AI and data platform with its lakehouse ethos.

“We are really finding this great intersection between the conversations around the lakehouse and the conversations around generative AI, because that’s what the Lakehouse offers,” said Joel Minnick (pictured), vice president of marketing at Databricks. “For the first time, uniquely, my AI platform and my data platform are the exact same thing. And that gives me such wonderful insight into the provenance, lineage and quality of my data [that] I just can’t get anywhere else.”

Minnick spoke with theCUBE industry analyst John Furrier at the Databricks Data + AI Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the data lakehouse’s evolution to support new-age AI.

Leaving the format wars in the past

One key objective at Databricks over the years is the propagation of a format-agnostic data environment. This AI-driven unification is simply the latest step to that goal, according to Minnick.

“For us, when it comes back to what has Databricks always been in pursuit of, it’s been in pursuit of the democratization of data and AI,” he explained. “When we thought about Delta Lake and the 10 years that it’s taken for the Lakehouse to mature to the place it is today, it was always about … there are all these proprietary data platforms out there, and that restricts what people can do. We should disrupt that. We should find a new way for folks to use data. And that was what led to Delta Lake and to knock those silos down.”

Subsequently, Databricks built a universal data format, UniForm, that allows Delta Lake to read from and write to every foundational lakehouse system no matter the vendor, Minnick explained. Just released with Delta Lake 3.0, it means that users can eliminate the complex integration work brought about by different data formats and simply focus on building the most performant lakehouses for their data.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Databricks Data + AI Summit:

Photo: SiliconANGLE