Organizations today are grappling with growing data volumes, diverse sources, and increasing complexity in their analytics needs. While Snowflake’s Data Cloud offers a highly scalable and powerful solution for managing and analyzing data, the challenge lies in ensuring that this data is consistently defined, easily accessible, and governed across the entire organization. This is where Cube’s universal semantic layer shines.

Snowflake’s Data Cloud is a fully-managed platform that provides elastic scaling, multi-cloud support, and a unified storage solution for structured and semi-structured data. It powers everything from data warehousing to real-time analytics, offering robust governance and security. However, transforming raw data into usable, consistent insights across the business requires an additional layer that simplifies how data is accessed, queried, and interpreted.

Cube provides one place to define business metrics, dimensions, and KPIs in a consistent way, ensuring data is interpreted uniformly across various teams, tools, and reports. It abstracts the complexity of Snowflake’s raw data and streamlines analytics for non-technical users, offering faster insights and more accurate decision-making.

A Perfect Pair: Combining the Power of Snowflake Data Cloud with Cube’s Universal Semantic Layer

Unify Your Data Architecture, Data Models, and Metrics

Snowflake’s Data Cloud allows businesses to store and manage vast amounts of data from multiple sources. But accessing this data in a consistent, meaningful way across the entire organization can be complex, especially when multiple teams and tools are involved.

By integrating Cube with Snowflake, companies create a centralized semantic layer where business definitions, metrics, and KPIs are standardized. This ensures that every department—whether it’s finance, sales, or marketing—is working from the same trusted data definitions, regardless of the analytics tool they use.

Improve Collaboration between Data and Business Teams

Snowflake’s infrastructure enables data engineering teams to efficiently manage data pipelines, ensuring that data is properly transformed and stored. However, there’s often a disconnect when business teams attempt to leverage this data for reporting and insights.

Cube bridges the gap between technical and business users by providing a business-friendly interface to the data stored in Snowflake. Business analysts can self-serve their analytics needs through BI platforms, such as Tableau, Looker, and Power BI, and spreadsheets, such as Microsoft Excel, while Cube ensures that all calculations and metrics are accurate, governed, and consistent across the board.

Gain Real-Time Insights at Scale

Snowflake is known for its scalable, high-performance capabilities, allowing companies to analyze data quickly and at scale. But speed and scalability are only part of the equation—consistent and meaningful data is what drives actionable insights.

Cube complements Snowflake by delivering real-time metrics at scale, enabling organizations to tap into their Snowflake data instantly with the confidence that they’re seeing reliable, consistent results. Together, they empower decision-makers to access the most up-to-date insights across any tool or dashboard, ensuring data-driven decisions are made in real time.

Achieve Faster Time to Insight and Lower Costs

The combination of Snowflake’s elastic architecture and Cube’s optimization features helps organizations reduce the time spent on data preparation and improve query performance.

Cube’s caching and pre-aggregation capabilities ensure that commonly used queries are accelerated, reducing the computational load on Snowflake. This not only speeds up time to insight but also optimizes infrastructure costs, allowing companies to fully leverage Snowflake’s capabilities without overburdening their budgets.

Establish Governance and Compliance Without Compromise

As businesses grow, the need for strong data governance and security becomes paramount. Snowflake provides a secure foundation for managing data, with built-in features like encryption, role-based access control, and auditing. However, maintaining governance and consistency in how data is used across multiple teams and tools is critical. Cube ensures that governance policies are consistently applied across all downstream tools, eliminating the risk of metric discrepancies or data misuse. This level of control allows organizations to meet industry regulations, such as GDPR and HIPAA, without compromising on data accessibility.

Together, Driving the Future of Data Analytics

By integrating Cube’s universal semantic layer with Snowflake’s Data Cloud, organizations can fully unlock the value of their data. Snowflake provides the infrastructure for storing, processing, and analyzing massive datasets, while Cube ensures that data is consistently defined and easily accessible for everyone in the organization.

This powerful combination helps teams:

  • Break down data silos, democratizing access to critical insights across the entire business.
  • Accelerate time to value by simplifying the process of preparing, serving, and analyzing data.
  • Ensure governance, security, and compliance, from ingestion to insight, across all data platforms and tools.

Whether you're generating real-time dashboards, building machine learning models, or enabling self-service analytics, the combination of Snowflake and Cube provides a reliable, scalable, and consistent data foundation for all your needs. Contact sales to learn more about how Cube and Snowflake work together.