A growing variety of corporations are using data models – abstract models that organize data elements and standardize their interconnections. However, as data analytics and the boom in artificial intelligence lead organizations to expect more data models, managing many of the old paradigms is proving difficult and extremely fragile.
At least that is what engineers and entrepreneurs Artem Keydunov and Pavel Tyunov observed in their work. While working at Starsbot, the data analytics startup they each co-founded in 2016, Keydunov and Tiunov often consulted with organizations struggling to get their “data house” in order.
Cube began as an open source project in 2019, offering what Keydunov describes as a “universal semantic layer” for organizational data that will be fed into databases, business intelligence (BI) tools, and even AI-based chatbots. Now, five years later, Keydunov and Tiunov have a real business on their hands, launching a subscription-based service built on Cube – Cube Cloud – that adds automated workflows and enterprise-centric management and deployment tools.
“There is no shortage of data,” Keydunov told TechCrunch. “The demand for data continues to grow among employees, partners and customers who are motivated by the idea that data-driven decisions lead to improved operational efficiency, increased customer satisfaction and competitive advantage. Technologies such as artificial intelligence, machine learning, the Internet of Things and blockchain are changing the data landscape and revolutionizing the way organizations collect, process and derive value from data. It’s not just people who need data; now machines need data too.”
Data modeling challenges aside, research suggests that relatively few organizations achieve even basic success by deriving value from their data. Gartner 2022 vote data analytics leaders said lower than half imagine their teams are effectively delivering value to employers. This is despite the incontrovertible fact that, based on the same survey, corporations spend an average of over $5 million on data management, governance and analytics initiatives.
So what to do? For Keydunov and Tyunov, the answer was to attempt to create a platform that would function a unified source of truth for all of the company’s data and metrics.
“Cube Cloud is a universal semantic layer that is an independent yet interoperable part of the modern data stack that sits between data sources and data consumers,” Keydunov said. “A universal semantic layer enables every data endpoint – whether they are BI tools, embedded analytics, AI agents and chatbots – to work with the same semantics and underlying data.”
Companies use Cube Cloud to build this semantic layer and connect it to varied applications and tools, using role-based access control, data caching, single sign-on, and scalable infrastructure as needed. Enterprise customers gain access to consultants who can train their data engineers to work with Cube Cloud and offer on-demand support, in addition to build an initial Cube Cloud instance – on Cube-owned servers or on-premises – tailored to the business.
“Cube Cloud automatically adjusts queries and enters the appropriate security context – user or role details – to ensure that only the right users have access,” adds Keydunov. “With performance analytics in Cube, customers can find redundant queries or other opportunities to cache and pre-aggregate query results, reducing the amount of computation required.”
Cube competes with AtScale, which also offers a semantic layer for data modeling and sharing, and Dtb Labs’ recently acquired Transform. But Cube appears to be doing well, with a customer base of greater than 200 Fortune 1000 brands and a user base of 5 million people, the company says.
Keydunov says downloads for the open-source Cube project have surpassed 10 million downloads, and Cube Cloud is currently installed on roughly 90,000 servers. From 2023 to 2024, the variety of bookings tripled and the average transaction size tripled.
Without a doubt, it was this success that attracted recent investments to the company. San Francisco-based Cube announced this week that it has raised $25 million from backers including Databricks Ventures, Decibel, Bain Capital Ventures, Eniac Ventures and 645 Ventures. Bringing the 40-person startup’s total funding to $48 million, the recent funds might be used to support Cube’s marketing and go-to-market efforts and expand Cube Cloud’s capabilities, Keydunov said.
“Our investors have encouraged us to raise capital to support the expansion of our go-to-market team so that we can take advantage of the massive growth in demand for AI and the semantic layer,” Keydunov continued. “We have noticed that companies are becoming more balanced and cautious in their assessments, which may slow down the sales process a bit, but gives us more time to prove our value against the competition. We are well capitalized with our new round of financing and have plenty of runway to grow the company to its next milestone.”