Home / Companies / Databricks Ventures seeks startups aligned with the ‘Lakehouse’

Databricks Ventures is off to a quick start.

Launched in December by Databricks Inc., the new venture arm aims to invest in young companies developing data and artificial-intelligence systems that work with its parent’s Databricks Lakehouse data repository and analytics platform. On Thursday, Databricks introduced a version of that platform for retailers, Lakehouse for Retail.

Andrew Ferguson, head of the new venture arm, is leading the strategic investment effort. Early this month, Databricks Ventures made its first investment, joining in a $110 million Series D funding round of Labelbox Inc., a San Francisco-based training data platform for enterprise machine-learning applications.

Mr. Ferguson talked with WSJ Pro AI about the Lakehouse Fund, the unit’s first, and the effort to identify startups that can contribute to Databricks’ “lakehouse" ecosystem. In the past, more common data repositories required companies to make copies of their data so that it could be structured and analyzed in a separate environment. A lakehouse allows users to analyze data in the repository itself, according to Databricks, a startup valued at $38 billion.

Corporate-backed venture capital funds can have strategies that go beyond the purely financial. “We can get value from the strategic angle and the joint customer relationships," Mr. Ferguson said. “We’re patient."

Edited excerpts follow.

WSJ Pro AI: It’s interesting to see a startup launch an investment fund to invest in other startups.

Mr. Ferguson: We’ve raised about $2.6 billion in capital in the last year or so. So even though we’re private, we’re very well capitalized and probably better capitalized than many companies that happen to already have gone public.

We’re investing off the company balance sheet in venture-backed, earlier-stage companies that are aligned with the Databricks and lakehouse ecosystem.

WSJ Pro AI: What type of companies?

Mr. Ferguson: It’s really any category where the product offering is complementary to Databricks. So that’s why Labelbox is a great example, because they’re in the data-labeling category. And they help companies take unstructured data [pieces of information that don’t readily fit into a database] and put some structure on it so they can—within the Databricks platform—analyze it more efficiently, so they get more value out of it.

Another example might be data-ingestion startups. Companies need to get data from wherever it happens to be stored—perhaps legacy systems, perhaps a cloud environment—into the Databricks platform.

Given the lakehouse is a relatively new category, and it’s not as established as some other categories like the original data lake or a data warehouse, we want to make sure that customers have a wide set of partners that they can work with to enable a wide variety of use cases within the lakehouse ecosystem.

WSJ Pro AI: How big will the fund grow and how many companies are you looking to invest in?

Mr. Ferguson: We don’t have specific targets.

We’re going to invest in as many good companies as fit our financial profile and strategic fit. We have one announced investment so far, and several more have been closed—although not yet announced.

WSJ Pro AI: How does Databricks Ventures work?

Mr. Ferguson: We’re not leading the financing rounds. The company has to be raising a Series A or Series B, or later. We’ll participate as a piece of that larger round.

And want to make sure that there’s a really high-quality product that the potential portfolio company is offering. So that we’re confident working on the integration, helping with the joint go-to-market promotion, and putting that product in front of our own customers. We have a fantastic set of technical experts in the field. And they can really help us with a lot of the technical vetting.

And they have to believe in the lakehouse ecosystem and want to contribute to it.

WSJ Pro AI: How does Databricks Ventures view the VC landscape?

Mr. Ferguson: Because we’re investing off the balance sheet, we have the luxury of not having to raise outside capital. But we did have some very thoughtful discussions to make sure that we had a very clear mandate for Databricks Ventures and we weren’t just going to be randomly sprinkling money around the ecosystem.

It’s an ebullient investing environment. But we’re patient. We don’t have to return the capital to investors at any point.

For us, we can get value from the strategic angle and the joint customer relationships in addition to a pure financial return on investment. So we can take a slightly different view of the environment than a purely financially oriented VC can.

WSJ Pro AI: Will Databricks launch a second fund?

Mr. Ferguson: I think so. I think it’s going to become very strategic over time. We launched with one fund and a specific mandate. And as we can prove our success, our ambitions will grow.

This story has been published from a wire agency feed without modifications to the text

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