Semantic Layer

Define metrics upstream to align your team.

Just like these companies:

The problem:
Inconsistent insights across your organization

The average enterprise uses more than six BI applications, but manually defining metrics in each one gets messy—leading to inconsistent insights, and cross-functional misalignment.

The solution:
Upstream, centralized metrics definitions

With upstream and centralized metrics definitions that are managed in code in Cube’s semantic layer, there is no need to specify metrics separately in each application.

Why Semantic Layer with Cube

Consistent definitions mean consistently aligned teams.

Cube’s Semantic Layer provides a centralized, single source of truth for every team’s data—
facilitating synchrony, efficiency, and productivity.

Consistency + Reliability

Inform every decision with accurate data with centralized metric definitions and upstream data modeling.

It’s time to combine business goals and modern software development techniques. Data engineers can use Cube’s Semantic Layer to manage metrics definitions following best practices: version control, automated end-to-end testing, code reviews, and isolated environments. And, consistent data across every downstream tool allows every decision-maker to strategize with shared knowledge.

YAML
JavaScript

views:
  - name: active_users
    description: "14 days rolling count of active users"
    includes:
      # Measure
      - users.rolling_count
      # Dimensions
      - users.is_paying
      - users.signup_date
    dimensions:
      - name: company_name
        sql: "{company.name}"
        type: string

Compatibility + Flexibility

Access your single source of truth in every downstream tool using Cube’s Postgres-compliant SQL.

The power of Cube’s Semantic Layer lies in its “headlessness” and universal compatibility. Cube’s foundation of Postgres-compliant SQL means that you won’t need to learn a new language to connect our data models, caching, and data access control layers to your tools. Every BI, visualization, and notebook tool can connect to Cube via our SQL API.

Speed + Performance

Accelerate your data stack with a semantic layer that includes caching.

Organizations face problems with latency when their teams use many different data apps, flooding data sources with direct, redundant queries. Cube sits between an organization’s data apps and its data sources, caches and pre-aggregates data, and orchestrates queries, creating a buffer between consumers and sources to uniformly speed queries and reduce costs.

Security + Governance

Enforce uniform security and governance with data access control that happens upstream of every data app.

Defining data access control upstream means that every data application is governed by the same security context. To prevent inconsistent data governance, Cube’s out-of-the-box multitenancy support and granular, role- and column-based data access control ensure only the right consumers see the right data.