Data Modeling

Centralize your data models upstream, so that you only have to define them once

Whether you’re building an internal application, a dashboard, or an embedded analytics application, ensure they’ll all be powered by consistent data—and skip manually orchestrating metrics for each presentation layer. Combined with Cube’s advanced caching and pre-aggregation capabilities, this ensures that every downstream app stays updated with the latest information—cost-effectively and with low latency.

Seamlessly integrate with every application

Cube’s integrations and Postgres-compliant SQL API, REST API, GraphQL API, MDX API, and DAX API ensure compatibility with every modern data stack. Plus, reduce development context switching: manage data models in your favorite language—Python, JavaScript, or YAML.

Reduce time-to-value with development tools

Cube Cloud enables team collaboration and rapid data model prototyping with a development toolkit that includes Data Model IDE, Cube Copilot, Playground, Development Mode, and more. Reduce time authoring and debugging the data model—and more time with your actionable insights.

Turn clicks into code within a shared workspace

Choose between the original code-first approach and visual data modeling with Cube's Visual Model Editor. Define data models collaboratively among data engineers and data analysts, bringing diverse perspectives to the table. Create and modify cubes and views, joins, relationships, dimensions, and measures visually.

How does the Cube data modeling technique work?

Building Your Data Model

Cube is a dataset-oriented semantic layer. When building your data model, you’ll deal with two types of objects: cubes and views.

Cubes represent business entities such as customers, line items, and orders. In cubes, you define all the hierarchies, calculations, and folders using dimensions and measures.

All cubes within your data model constitute your data graph.

YAML
JS

views:
  - name: active_users
    description: 14 days rolling count of active users

    includes:
      # Measure
      - users.rolling_count
      # Dimensions
      - users.is_paying
      - users.signup_date
      - name: company.name
        alias: company_name

Views expose slices of your data graph.

You have full control over which measures and dimensions are exposed to BIs or data apps and the direction of joins between exposed cubes.

Cube’s core data modeling concepts can be easily grasped by former Looker users, familiar with LookML syntax.

YAML
JS

views:
  - name: active_users_view
    public: COMPILE_CONTEXT.security_context.is_finance

    cubes:
      - join_path: active_users
        includes:
          - weekly_active
          - time

      - join_path: accounts
        includes:
          - pricing_plan

Data Modeling - Two Types of Objects

Canvas, formerly known as Data Graph, visualizes cubes and views with joins and relationships between them as an entity-relationship diagram (ERD). Get a a bird's-eye view of the data model, with the added capabilities of adding and modifying them visually.

Canvas - Improve collaboration across teams, while modeling your data visually

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Check out the rest of Cube's four-part semantic layer

Data APIs

Make your data accessible and your stack compatible.

Data Access Control

Robust governance begins with centralized permissions management.

Caching and Data Performance

Rely on uniformly performant data with centralized caching.

Related Case Studies

See Cube's data access control in action

How Cloud Academy sped up data modeling by 5x and decreased analytics downtime by 90%

Read the Case Study

How Relata built completely native embedded analytics that runs 85% faster.

Read the Case Study

With Cube, we’ve reduced the time required to generate real-time and historical reports from 10’s of seconds to less than 2, while reducing our spending on hosting by almost 80%.

Brandon Ellis

Co-Founder & CTO at Cuboh

We found an incredibly fast-to-market and flexible modern analytics solution: Cube. With it, we were able to deliver a highly customized and intuitive embedded analytics user experience—in two weeks, and without changing our stack.

Dhyan Shah

Product Manager at RamSoft

We didn’t want to use Tableau, Power BI or Looker Studio. We needed our own tool integrated completely within our platform, Cube allowed us to do just that.