When is Databricks going public? 'Six months at a time,' says CEO Ali Ghodsi.

Armed with a fresh $1.6 billion, Databricks is trying to fend off competitors to own the data lakehouse market as it preps for an IPO.

​Databricks CEO Ali Ghodsi speaks during a 2019 Bloomberg Television interview.

Databricks CEO Ali Ghodsi believes Databricks is already well on the path to becoming a public company.

Photo: David Paul Morris/Bloomberg via Getty Images

It's a big question in enterprise tech right now: When will Databricks go public? The answer will have to wait.

The startup just raised another huge round of private capital ahead of its IPO, which could happen this year. The new $1.6 billion Series H round values Databricks at a jaw-dropping $38 billion. New investors include BNY Mellon, ClearBridge and the University of California's investment fund.

Money is no object in Silicon Valley these days, but to put that in perspective: There's only a handful of private startups, including TikTok owner ByteDance, that are valued higher. It's even more wild when considering that the products Databricks sells are deeply technical and targeted towards skilled developers. In other words: This isn't teenagers dancing to snippets of Olivia Rodrigo's album, it's heavy-duty artificial intelligence.

The latest fundraising round won't impact its IPO timeline, per CEO Ali Ghodsi. In fact, he believes Databricks is already well on the path to becoming a public company.

"We're going public six months at a time," Ghodsi told Protocol. "Usually when you IPO, you want to make sure you are getting the long-term investors … [and] we're basically allocating the big blocks of allocations to the big mutual funds and other investors right now," he added.

The investors in question are Franklin Templeton, which participated in February's $1 billion round, and Morgan Stanley, which was involved in the latest one. Another key audience that is likely watching the IPO timeline with great interest? Databricks employees.

When you look at the last few funding rounds, "it's not a lot of dilution, single digit percentages," Ghodsi said. "The company is getting more diluted by the people we hire every year … [and] that dilutes the company more than fundraising."

Databricks has offered "multiple liquidity events for our employees," per Ghodsi.

And a key reason Databricks needs to keep the continual flow of capital is to establish the data lakehouse — an architecture it created that blends together the data warehouse and the data lake — as a permanent category, as well as fend off competition from upstarts and the cloud giants. That means hiring pricey engineers and pouring money into research and development, among other costly undertakings.

"Building a whole data and AI stack, creating a new category, it's going to take a lot of investment," said Ghodsi. "We love the cloud vendors ... but there is also overlap with them. There is Snowflake. If you look at the market, all of those are massive companies with massive balance sheets."


Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

Keep ReadingShow less
Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Keep ReadingShow less
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.

AWS CEO: The cloud isn’t just about technology

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.

Photo: Noah Berger/Getty Images for Amazon Web Services

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.

It will be the second re:Invent with CEO Adam Selipsky as leader of the industry’s largest cloud provider after his return last year to AWS from data visualization company Tableau Software.

Keep ReadingShow less
Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Keep ReadingShow less
Bennett Richardson

Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.


Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.

Keep ReadingShow less
Kate Kaye

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

Latest Stories