Cube

What is Cube?

Cube is an agentic analytics platform built on a universal semantic layer that centralizes business logic and data definitions across an organization's entire data ecosystem. It allows teams to model their data once in a declarative format and deliver it consistently to any analytics tool, dashboard, or application through various APIs. The platform emphasizes four core pillars: data modeling, access control, caching and pre-aggregation, and API integration. By consolidating metric definitions into a single source of truth, Cube eliminates the need to write duplicate queries across different tools and ensures consistent data interpretation organization-wide. The platform integrates seamlessly with cloud data warehouses, supports real-time and batch data processing, and includes an AI API for natural language queries with LLMs.

Key Features

• Universal Semantic Layer

  Centralized data modeling layer that defines metrics and business logic once in declarative YAML format, ensuring consistent interpretation across all downstream analytics tools and applications.

• Advanced Caching & Pre-Aggregation

  In-memory caching system and automated pre-aggregation capabilities that accelerate query performance, reduce database load by up to 50%, and significantly lower compute costs.

• Multi-API Integration

  Comprehensive API suite including REST, GraphQL, SQL, Orchestration, and AI APIs that enable seamless data delivery to BI tools, embedded analytics, LLMs, and custom applications.

• Granular Access Control

  Row-level and column-level data security controls that ensure users only access authorized data while maintaining centralized governance and compliance.

• Developer-Friendly Workflow

  Software engineering best practices including Git versioning, CI/CD pipelines, isolated development environments, code review processes, and automated testing for data models.

• Real-Time & Historical Analytics

  Unified querying interface that seamlessly merges streaming and batch data sources, enabling analysis of both real-time and historical data within a single query.

Use Cases

• Embedded Analytics : SaaS companies can build customer-facing dashboards and reporting features with consistent metrics, fast query performance, and secure multi-tenant data access.
• Enterprise BI Standardization : Large organizations can eliminate metric discrepancies across departments by establishing a single source of truth for business definitions used by all BI tools.
• Agentic AI Applications : Development teams can integrate LLMs with structured data through Cube's AI API, enabling natural language queries that return accurate, governance-compliant results.
• Data Engineering Efficiency : Data teams can reduce repetitive SQL writing and maintenance burden by defining metrics once and reusing them across hundreds of dashboards and reports.
• Cloud Data Warehouse Optimization : Organizations can dramatically reduce cloud compute costs by leveraging pre-aggregations to minimize expensive warehouse queries while maintaining sub-second response times.

FAQs