More and more businesses are adopting data models – abstract models that organize elements of data and standardize how they relate to each other. But as the boom in data analytics and AI increases organizations' demands on data models, many of the old paradigms are proving to be difficult to manage and highly fragile.
At least that's how engineers and entrepreneurs Artyom Kaydunov and Pavel Tyunov felt in their work. During their time at Starsbot, the data analytics startup they co-founded in 2016, Kaydunov and Tyunov frequently consulted with organizations struggling to outfit their so-called “data houses.”
Cube started as an open-source project in 2019 to provide what Keydunov calls a “universal semantic layer” for organizational data that could feed databases, business intelligence (BI) tools, and even AI-powered chatbots. Now, five years later, Keydunov and Tiunov have gone full-scale and launched Cube Cloud, a subscription-based service that builds on Cube and adds automated workflows and enterprise-grade governance and deployment tools.
“There's no shortage of data,” Kidunov told TechCrunch. “Demand for data continues to grow among employees, partners and customers, motivated by the idea that data-driven decisions lead to improved operational efficiency, higher customer satisfaction and competitive advantage. Technologies such as AI, machine learning, the Internet of Things and blockchain are reshaping the data landscape and revolutionizing how organizations collect, process and derive value from data. It's not just humans who need data. Now machines need data too.”
Data modeling challenges aside, research suggests that relatively few organizations are achieving even a basic level of success in deriving value from their data. In a 2022 Gartner survey of data analytics leaders, fewer than half believe their teams are effectively delivering value to their employers. This is despite the fact that companies are spending an average of more than $5 million on data management, governance and analytics efforts, according to the same survey.
So how do they do it? For Keydunov and Tiunov, the answer was to create a platform that would serve as a unified source of truth for all the company's data and metrics.
Diagram of Cube's semantic data layer. Image courtesy of Cube
“Cube Cloud is a universal semantic layer that sits between data sources and data consumers, part of an independent yet interoperable modern data stack,” said Keydunov. “The universal semantic layer enables any data endpoint, be it a BI tool, embedded analytics, AI agents or chatbots, to work with the same semantics and underlying data.”
Companies use Cube Cloud to build a semantic layer, connect to various apps and utilities, and employ role-based access control, data caching, single sign-on, and extended infrastructure wherever needed. Enterprise-level customers can train their data engineers to work with Cube Cloud, access consultants who can provide on-demand support, and even build their first Cube Cloud instance (on Cube-owned servers or on-premise) tailored to their business.
“Cube Cloud automatically tunes queries and injects the appropriate security context (user or role details) to ensure that only the right users have access,” adds Keydunov. “And through Cube's performance insights, customers can find redundant queries and other opportunities to cache and pre-aggregate query results, reducing the amount of compute required.”
Cube competes with AtScale, which also offers a semantic layer for data modeling and serving, and Transform, which was recently acquired by Dtb Labs, but it appears to be holding its own, with a customer base that spans more than 200 Fortune 1000 brands and nearly 5 million users, according to the company.
According to Kidunov, the open source Cube project has been downloaded more than 10 million times, Cube Cloud is currently installed on about 90,000 servers, the number of reservations is expected to grow threefold from 2023 to 2024, and the average deal size is also growing threefold.
That success has no doubt attracted new investment to the company: San Francisco-based Cube announced $25 million in funding this week from investors including Databricks Ventures, Decibel, Bain Capital Ventures, Eniac Ventures and 645 Ventures. The 40-employee startup has raised a total of $48 million in funding, and the new money will be used to support Cube's go-to-market and marketing efforts, as well as to expand the capabilities of Cube Cloud, Keydunov said.
“Investors encouraged us to raise equity to help expand our go-to-market team so we can capitalize on the surge in demand for our AI and semantic layers,” Keydunov continues. “Enterprise businesses are becoming more cautious and careful in their valuations, which may slow down the sales process a bit, but it also gives us more time to prove our value against competitors. We are well capitalized with our new funding round, giving us enough headroom to grow the company to its next milestone.”