Cube unveiled an expanded partnership with Microsoft, launching new integrations on Tuesday between its semantic layer and both Power BI and Excel that enable joint customers to better access data for analysis.
Based in San Francisco, Cube is a 2019 startup whose Cube Cloud platform is a semantic layer designed to enable users to eliminate isolated data, establish consistent models and governance, simplify access and exploration, discover data for reuse and easily integrate with APIs.
Data Analysis Expressions (DAX) API for Power BI integrates Cube’s semantic layer with Power BI so that joint customers can access live data in cloud data warehouses directly from Power BI using DAX, which is Power BI’s native query language.
Cube Cloud for Excel Add-in, meanwhile, uses Cube’s Multidimensional Expressions (MDX) API to connect governed data with Excel so users can update spreadsheets with a single click to analyze current data.
The ability to query cloud data warehouses with DAX really is a breakthrough for teams who have struggled to make Power BI work with their preferred data warehouse platforms.
Donald Farmer Founder and principal, TreeHive Strategy
Accessing live data in data warehouses has been a struggle for many Power BI users, according to Donald Farmer, founder and principal of TreeHive Strategy. As a result, Cube’s new integration with Power BI and its semantic layer is a significant addition for joint Cube and Microsoft customers.
“The ability to query cloud data warehouses with DAX really is a breakthrough for teams who have struggled to make Power BI work with their preferred data warehouse platforms,” Farmer said, noting that even using Power BI with Microsoft’s own Fabric platform has been a struggle for data architects and engineers.
Regarding the integration between Cube’s semantic layer and Excel, Farmer added that it is also significant, given that it simplifies connections between Excel and cloud data storage platforms.
“The Excel connectivity is also an excellent addition,” he said.
Cube and Microsoft first partnered in March 2024.
New capabilities
Data architects and engineers have long been able to connect Power BI and Excel directly to data warehouses such as Azure or third-party platforms including Databricks and Snowflake.
Such direct connections, however, don’t always work smoothly.
Microsoft developed MDX in the late 1990s to connect analytics tools with multidimensional online application processing (OLAP) cubes. DAX was developed later by Microsoft as an intended improvement and became the query language for Power BI.
Neither, however, is the current industry standard. Instead, SQL has become the query language for most analytics and data warehouse platforms.
Before Cube’s new integrations, Power BI and Excel users had to either copy and move data from data warehouses via import mode or MDX and DAX needed to be translated to SQL in Microsoft’s DirectQuery mode. Copying and moving data can be labor intensive, while translations from Microsoft’s query languages to SQL are not always seamless, which leads to a lack of performance, according to Artyom Keydunov, Cube’s founder and CEO.
The integrations between Cube’s semantic layer and Microsoft’s analytics platforms are intended to address query performance.
“The SQL generated is frequently unoptimized and performs poorly,” Keydunov said. “Our aim with the [the integrations] is to improve this performance.”
Given that the integrations improve query performance between Power BI and Excel and cloud data warehouses, they address two major trends, according to Kevin Petrie, an analyst at BARC U.S.
One is the sustained popularity of spreadsheets. The other is that data remains highly distributed despite the efforts of cloud data platforms to help organizations consolidate. As a result, the integrations are significant.
“This announcement gives companies a useful method of analyzing data,” Petrie said. “Analysts and data teams of all types need to access distributed data wherever it resides in order to drive decisions and support increasingly advanced models.”
Like Keydunov, Farmer noted that direct connections between Microsoft’s analytics platforms and data warehouses often have poor results. In addition, for those choosing to use DirectQuery, costs can add up, he continued.
Cube’s semantic layer enables users to cache data, which results in more efficient access from Power BI and Excel. And it enables such access via DAX and MDX.
“By enabling connectivity with both these standards, Cube has created a modern OLAP solution, which is a real breakthrough in engineering for them,” Farmer said.
Regarding the impetus for integrating Cube’s semantic layer with Power BI and Excel, customer feedback was a significant factor, according to Keydunov.
Power BI is perhaps the most widely used business intelligence platform, with more than 12 million users. Excel, meanwhile, remains the most popular tool for business analysis with more than 750 million users.
“The continued investments in new Microsoft integrations are a direct response to enterprise customer demand for these capabilities,” Keydunov said.
Future plans
In addition to its partnership with Microsoft, Cube is partners with AWS, Google Cloud, Databricks and Snowflake.
However, despite integrations with prominent data platform vendors, Cube’s semantic layer platform is a relative newcomer compared with others providing similar capabilities such as AtScale, GoodData, Looker and MicroStrategy. In addition, its total funding of $46.7 million, including $25 million in 2024, is far less than other competitors such as DBT Labs.
To compete, one of Cube’s goals is to continue modernizing OLAP, according to Keydunov. Another is to emerge as a catalyst for AI adoption by enabling customers to turn raw cloud data into AI-ready data.
“With well-defined semantic modeling, it becomes possible to deliver consistent, reliable and trustworthy AI outputs and autonomous actions,” Keydunov said.
That focus on supporting AI platforms is wise, according to Petrie.
Universal semantic layers are a valuable way to unify access to distributed data, he noted. Migrating data, data sovereignty requirements and security risks often prevents organizations from consolidating data in one location. Semantic layers help address that sprawl.
Access to data, meanwhile, is critical for AI development. Cube now supports such platforms as the LangChain framework. However, there are opportunities to integrate with others, according to Petrie.
“I would recommend they consider extending their support to include other AI/ML platforms, such as Dataiku and Domino Data Lab,” he said. “Data scientists need easy access to structured data as they train advanced models and put them into production.”
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
Cube Expands Partnership with Microsoft to Enhance Power BI and Excel Integrations
Cube has strengthened its collaboration with Microsoft by launching new integrations that connect its semantic layer with Power BI and Excel, improving data accessibility and analysis for joint users.
What is Cube?
San Francisco-based Cube, founded in 2019, offers Cube Cloud, a semantic layer platform that eliminates data silos, ensures consistent data governance, and simplifies data access and exploration. With seamless API integrations, Cube enables users to discover and reuse data efficiently.
New Microsoft Integrations: Power BI & Excel
Power BI Integration: Faster Access with DAX API
Cube’s Data Analysis Expressions (DAX) API for Power BI allows direct access to live cloud data warehouses using DAX, Power BI’s native query language. This enables users to query data in real-time without moving or duplicating data.
Excel Integration: Simplified Data Connectivity with MDX API
The Cube Cloud for Excel Add-in leverages Multidimensional Expressions (MDX) API, ensuring that Excel users can refresh spreadsheets with a single click, keeping their data current and accurate for seamless analysis.
Why These Integrations Matter
Accessing live data in cloud warehouses has been a major challenge for Power BI users, according to Donald Farmer, founder of TreeHive Strategy. Cube’s semantic layer bridges this gap, enhancing both query performance and data consistency across Power BI and Excel.
“The ability to query cloud data warehouses with DAX is a breakthrough for teams struggling to make Power BI work with their preferred data warehouse platforms.” — Donald Farmer
The Challenge with Power BI & Excel Data Connections
While Power BI and Excel already connect to Azure, Databricks, Snowflake, and other data warehouses, these connections often result in: ✅ Slow query performance ✅ High data transfer costs ✅ Complex SQL translations
Before Cube’s integrations, users had to:
Manually move data from warehouses (import mode).
Rely on Microsoft’s DirectQuery mode, which converts MDX/DAX to SQL—a process that often lacks efficiency and speed.
Cube’s Solution: Optimized Query Performance
Cube’s semantic layer optimizes query performance by eliminating inefficient SQL translations and providing cached data access, improving speed and cost-efficiency for Power BI and Excel users.
Industry Impact: Key Trends & Benefits
According to Kevin Petrie, an analyst at BARC U.S., Cube’s new integrations align with two major industry trends:
Spreadsheets remain highly popular for data analysis.
Data remains highly distributed, making seamless access crucial.
Cube’s enhancements provide businesses with a powerful method for data analysis, enabling analysts to access data anywhere while supporting advanced AI models and decision-making.
“Analysts and data teams need access to distributed data to drive decisions and support advanced models.” — Kevin Petrie
Cost Savings & Performance Boosts for Businesses
Cube’s cached data storage minimizes DirectQuery costs, making data retrieval faster and more efficient.
“By enabling connectivity with both MDX and DAX, Cube has built a modern OLAP solution, which is a real engineering breakthrough.” — Donald Farmer
Growing Demand for Power BI & Excel Integrations
Power BI: Over 12 million users worldwide.
Excel: Over 750 million users, making it the most widely used business analysis tool.
Cube’s continued investments in Microsoft integrations directly address enterprise demand for seamless, real-time data analysis.
Future Roadmap: AI & Data Unification
Beyond Microsoft, Cube partners with AWS, Google Cloud, Databricks, and Snowflake. However, Cube faces competition from AtScale, GoodData, Looker, and MicroStrategy, which offer similar semantic layer capabilities.
Expanding AI & ML Capabilities
To stay competitive, Cube aims to:
Modernize OLAP for enhanced analytics.
Drive AI adoption by transforming raw cloud data into AI-ready insights.
“With well-defined semantic modeling, AI outputs can be more consistent, reliable, and trustworthy.” — Artyom Keydunov, Cube CEO
Enhancing AI/ML Platform Integrations
Currently, Cube supports LangChain but could expand to other AI/ML platforms like Dataiku and Domino Data Lab, according to Kevin Petrie.
“Data scientists need structured data for training AI models, and Cube can further its AI impact by integrating with more AI platforms.” — Kevin Petrie
Final Thoughts
Cube’s expanded Microsoft partnership is a major step forward in improving data access, analytics, and AI readiness. By enhancing Power BI and Excel integrations, Cube enables businesses to unlock real-time insights, streamline workflows, and optimize data-driven decision-making.
💡 Looking ahead, Cube is poised to play a crucial role in AI-driven analytics and semantic layer advancements across the industry.
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