Organizations are dealing with ever-growing data volumes, diverse sources, and complex analytics needs. While platforms like Databricks’ Lakehouse provide an exceptional foundation for scalable, unified storage and powerful data processing, the next challenge is ensuring that this data is accessible and consistently defined across an entire organization. That’s where Cube’s universal semantic layer comes in.

Databricks Lakehouse is a next-generation architecture that combines the best features of data lakes and data warehouses into a single platform. It provides a unified data storage layer for structured, semi-structured, and unstructured data. With Databricks, companies can store vast amounts of data and process it using high-performance data engineering, data science, and machine learning capabilities. It supports a variety of workloads—from real-time streaming to advanced analytics—while ensuring governance and data quality.

On the other hand, Cube offers a universal semantic layer that brings consistency, governance, and efficiency to how data is queried and analyzed. It acts as a bridge between raw data and end-user tools like AI, BI, spreadsheets, and embedded analytics. Cube abstracts complex data into easy-to-understand metrics and definitions, so business users can trust and easily explore data without needing to worry about inconsistent definitions.

A Perfect Pair: Combining the Power of Databricks Lakehouse with Cube’s Universal Semantic Layer

Unify Your Data Architecture, Data Models, and Metrics

Databricks Lakehouse allows companies to store vast amounts of data from multiple sources, but accessing and interpreting this data can be complex, especially when involving multiple teams across an organization. Cube simplifies this complexity by creating a centralized semantic layer that defines metrics, dimensions, and KPIs in a consistent way. By integrating Cube with Databricks, all business units—whether marketing, sales, or finance—access the same governed data definitions, regardless of the tool they’re using.

Improve Collaboration between Data and Business Teams

With Databricks Lakehouse, data engineering teams can efficiently manage data pipelines, ensuring data is cleansed, transformed, and stored. However, there’s often a gap when business teams try to make sense of this data for decision-making.

Cube’s universal semantic layer bridges this gap, providing a user-friendly interface that democratizes data access. Analysts and business users can self-serve their analytics needs using BI platforms, such as Tableau, Looker, or Power BI, and spreadsheets, such as Microsoft Excel or Google Sheets, while relying on the consistent models and metrics powered by Cube.

Gain Real-Time Insights at Scale

Databricks is known for its real-time processing capabilities, enabling businesses to harness data streams for up-to-the-minute insights. But real-time data is only useful if it’s delivered with context and meaning. Cube excels at delivering consistent metrics at scale, allowing for real-time analytics across a variety of technology and tools. With Cube integrated with the Databricks Lakehouse, organizations can leverage both real-time data processing and consistent data, ensuring that decision-makers are always acting on the latest, most accurate numbers.

Achieve Faster Time to Insight and Lower Costs

The combination of Databricks’ scalable architecture and Cube’s ability to deliver simplified, governed data definitions means organizations can reduce time spent on data preparation and ensure faster delivery of insights. Cube’s caching and pre-aggregation features optimize query performance, reducing the computational burden on Databricks Lakehouse, lowering infrastructure costs, and improving the user experience.

Establish Governance and Compliance Without Compromise

As organizations scale, maintaining data governance becomes critical. Databricks Lakehouse offers a strong foundation for data governance, security, and compliance. However, as data is exposed to different teams and tools, maintaining consistency in how this data is used and reported becomes a challenge.

Cube’s universal semantic layer ensures that governance policies are enforced uniformly across all downstream analytics tools, eliminating the risk of data misuse and ensuring compliance with industry regulations.

Together, Driving the Future of Data Analytics

By combining the power of Databricks Lakehouse and Cube’s universal semantic layer, organizations can unlock the full potential of their data. Databricks ensures that data is stored, processed, and analyzed at scale, while Cube ensures that the data is consistently and easily accessible for the entire business.

With this “better together” solution, teams can:

  • Break down data silos, enabling unified, democratized access to critical insights.
  • Accelerate time to value by reducing the complexity of preparing and serving data.
  • Ensure governance, security, and compliance across the entire data lifecycle, from ingestion to insight.

Whether you're building machine learning models, powering real-time dashboards, or generating self-service reports, the combination of Databricks and Cube helps you achieve faster, more reliable, and more scalable data insights—for everyone. Contact sales to learn more about how Cube and Databricks work together.