Cloud-based data intelligence platform, Databricks, has come a long way since its inception in 2013 as a California University of Berkley startup to becoming one of a handful of technology companies that continue to defy increasingly challenging market conditions. Today, the company is valued around $62bn with some 60% of Fortune 500 companies relying on its data intelligence platform for big data processing, engineering, data science, and machine learning.

Databricks is riding the AI wave with a strategy of global expansion. On 25 April, the company unveiled plans to invest more than $250m in India over the next three years. The investment will include growing the company’s India based workforce by more than 50% to over 750 employees by the end of the current fiscal year.

At the start of 2025, the company adopted a country-first management model to address rapid overseas customer growth. In November 2024, in light of 70% annualised growth for its French business, the company opened a new dedicated office space in La Fondation in central Paris for its growing team of over 150 employees. The company’s European presence is supported by its engineering hub in the Netherlands which has also scaled to meet demand.

Similarly, Databricks’ rapid revenue growth in the UK has seen it evolve from the company’s first overseas presence in serviced offices near London’s Baker Street to become the company’s European headquarters with a headcount of over 400 people.

Databricks’ hope for a UK AI hub

Michael Green became Databricks’ UK managing director at the beginning of the year after having led the company’s Northern European business for the previous three years. Green’s vision for the company’s office in London’s Fitzrovia, overlooking the city’s iconic BT Tower, is for an AI hub serving both UK customers and partners, “a sort of salon for AI and innovation”. Indeed, the company has recently expanded its strategic partnerships with Anthropic, Meta, Palantir, and SAP.

Green’s optimism about the UK’s global role in the direction of travel for AI is hard to dampen and he’s excited for Databricks to share in this journey, something he describes as “doubling down on what we are doing in the UK.”

On external business factors that have the potential to threaten the company’s UK expansion, including a change in government and tariff chaos from the US, Green is assured that business’ commitment to AI transformation will continue no matter what. “The need to be focused on your data and AI strategy has never gone away, if anything, that’s accelerated in all organisations,” says Green.

Green’s hypothesis has been proved correct, so far, evidenced by the company’s consistent customer growth in the UK. In fact, the company’s UK business has not only remained resilient, but has not seen revenue growth below 60% – making it the company’s largest market outside the Americas.

The platform helps businesses build, deploy, share, and maintain enterprise-grade data solutions at scale. It’s built on Apache Spark—the open source analytics engine project built by Databricks’ founders—and uses its proprietary Lakehouse architecture to manage both structured and unstructured data.

Green is particularly effusive about Databricks SQL, a service that brings data warehousing capabilities to existing data lakes. It leverages open formats and standard ANSI SQL, offering an in-platform SQL editor and dashboarding tools for data analysis and collaboration within the Databricks workspace. It’s built on lakehouse architecture, which unifies data, analytics, and AI, and is part of the Databricks Data Intelligence Platform.

“We’ve got world class cloud data warehousing, and we don’t really talk about that enough. I think a lot of customers see Databricks’ strength in data engineering and analytics, but actually we’ve got this amazing DBSQL warehouse,” he says.
While data warehousing does not capture the imagination or headlines, most engineers will agree that a successful AI application is only as good as the data it is built on.

You’re only as good as your data

For an enterprise AI transformation strategy to work, a data strategy must come first. “Some organisations have ten, twenty or thirty years’ worth of disparate systems. Making sure all your data is in one location to make sure that you get your cloud data warehouses and your structured and your unstructured data talking to one another in one unified platform is key,” says Green. In essence, this is what Databricks’ Lakehouse architecture facilitates.

“Without that, you’re connecting to lots of disparate systems you’re trying to get information out. You’ve probably got a lot of duplicate data that’s doing that, or you’ve got incorrect data,” he says noting that most CIOs and organisations tell him that this has become the de facto norm.

A cloud data warehouse coupled with Databricks’ Lakehouse architecture creates a true unified data platform, according to Green. “From that point onwards, you then start to actually move that into that secure govern layer, which then goes into AI which creates a data intelligence platform. This is what’s really in everyone’s mind now, because they want to get proper, trusted AI in their organisations,” he explains.

The enterprise focus over the last couple of years has shifted from AI hype to tangible utility. “We saw people spending an inordinate amount of time, investment money, in POCs, with 70% that would never go into production,” says Green.

“Because if you didn’t actually know what you really wanted, you just threw something into AI, and you got an outcome that you didn’t really fully understand, or actually really trust. You have to know that your data is trusted otherwise it becomes meaningless. Enterprises need to ask themselves if their AI is going to help them to create a USP within their market? And so much of that comes down to their private data,” he says. Which brings Green back to his original point: that data is at the heart of everything.