The past few months have highlighted that the battle between Snowflake Inc. and Databricks Inc. is as hot as ever.

Snowflake vs. Databricks and the two companies’ efforts to maintain their moats was one of the subjects of conversation for theCUBE Research industry analysts John Furrier (pictured, left) and Dave Vellante (right) to discuss on the latest episode of the CUBE podcast.

The two companies have different philosophies. But there’s no question that Databricks is growing substantially faster than Snowflake right now, according to Vellante.

“We see that in the ETR data, and we’ve been reporting that for well over a year now,” he said. “The other thing is … Snowflake bundles in AWS revenue and passes that on. It is part of their revenue number, whereas Databricks is a cleaner Databricks,” he said.

In other words, Databricks doesn’t include revenue from Amazon Web Services Inc. and other cloud players. That may change with serverless, Vellante added.

“The point is that if you took away the Snowflake revenue that comes from AWS, they’d probably be a lot closer in size, and Databricks is growing faster,” he said.

Open-source strategy colors Snowflake vs. Databricks

During Snowflake’s recent Data Cloud Summit, the company outlined its strategy to open-source the Polaris Catalog. Polaris is essentially a technical metadata catalog only, Vellante noted.

“[Snowflake] Horizon is actually where you get role-based access control and all the heavy governance, but that’s Snowflake proprietary. The big question we asked is, ‘OK, well, how far is Snowflake going to go with open source? Are they eventually going to bring Horizon-like capabilities to open source? If they do, then where’s their moat?’” Vellante asked. “That was the big question that we were asking.”

Meanwhile, Databricks asserts Unity will combine together what Polaris and Horizon do separately. Plus, the company recently acquired Tabular Technologies Inc., developer of a universal storage platform based on the Apache Iceberg standard. That represents Databricks saying that it is all-in on Iceberg, according to Vellante. And, the company is all in on open-table formats and plans to bring that capability to open source.

“That is a big hit … to Snowflake,” Vellante said. “The reason why this is so important … this is so nuanced. Snowflake popularized the separation of compute from storage so that you could run it in the cloud, infinite compute, infinite storage. You didn’t have to buy it in chunks, in clusters.”

That meant companies could separate and scale things up or down and turn it off when it wasn’t needed. What Databricks is doing is enabling any compute engine to work on any data, Vellante noted.

“As long as you’re embracing open table formats, you can bring any compute engine to any data. We’re separating compute from data, not just compute from storage,” Vellante said. “What does that mean? That means if your moat was the fact that you had everything inside of Snowflake, governed and managed, and you can now do that in an open table format, that shifts the moat toward governance and then maybe applications, gen AI capabilities, product features, etc.”

Generative AI investment big subject of conversation

No matter what conversations have taken place on show floors in recent months, they’re all colored by one main subject — the next wave of generative AI. One major subject of conversation involves generative AI investment, discussed on the recent debut edition of the AI Insights and Innovation podcast with David Linthicum, principal analyst for theCUBE Research.

“I’ve been seeing this in the marketplace, and people I’ve talked to, bifurcation of budgets — either non-gen AI and gen AI projects are going to get funded,” Furrier said. “Gen AI is getting more funding, so it’s got to show how it’s going to contribute to that. But what David’s saying is that there’s not a lot of stuff blooming right now on the ROI side. So, there’s value there. And he pointed to some reports … the enterprise is rethinking this, in his mind. Use cases.”

There’s likely to be a longer sautéing of the market until use cases come out, according to Furrier. But companies are definitely taking action.

“RAG is out there heavily, more than people think. Most of the work’s being done in the data engineering piece. That’s why governance is so hot right now; that’s why we spent a lot of time with the Snowflakes, Databricks and the war there,” Furrier said. “A lot of companies don’t have a full data-engineered environment. So, they’re doing things in pockets.”

Watch the full podcast below to find out why these industry pros were mentioned:

Lou Gehrig, former professional baseball first baseman
Ali Ghodsi, co-founder and CEO of Databricks
Sridhar Ramaswamy, CEO of Snowflake
Michael Scarpelli, CFO of Snowflake
Matei Zaharia, co-founder and CTO of Databricks
Ryan Blue, co-founder and CEO of Tabular
Matt Baker, SVP of activating AI strategy at Dell Technologies
Mark Albertson, senior writer at SiliconANGLE
Dave Linthicum, enterprise technology analyst at SiliconANGLE — theCUBE Research
Jeremy Burton, CEO of Observe
Charlie Kawwas, president at Broadcom
Jensen Huang, founder and CEO at Nvidia
Rob Strechay, principal analyst at theCUBE Research
George Gilbert, principal analyst, data and AI at theCUBE Research
Savannah Peterson, founder and chief unicorn at Savvy Millennial and host of theCUBE
Frank Slootman, chairman of the board of directors at Snowflake
Benoit Dageville, co-founder and president of product at Snowflake
Bill Walsh, former NFL coach
David Floyer, analyst emeritus at theCUBE Research
Hock Tan, president and CEO of Broadcom
Gee Rittenhouse, VP at Amazon Web Services
Mark Terenzoni, GM of security services at AWS
Merritt Baer, CISO of Reco AI
Holger Mueller, VP and principal analyst at Constellation Research
Bob O’Donnell, president, founder and chief analyst at TECHnalysis Research

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Photo: SiliconANGLE