The Semantic Layer
for building data apps

Trust your insights. Consume data from any source, organize it into consistent metrics, and use it with every data app.

What is a Semantic Layer?

A semantic layer is a middleware between your data source and your data application. Cube is an API-first, four-part semantic layer that enables data engineers and developers to make their data consistent, secure, performant, and accessible across every application.
More about Cube’s Semantic Layer

How does Cube’s Semantic Layer work?

Data Modeling

With an upstream semantic layer, create a centralized, single source of truth—with consistent metrics you only need to define once.

Cube uses Data Schemas to model raw data into meaningful business definitions and pre-aggregate data for optimal results. More about Data Modeling

YAML
JS

cubes:
  - name: active_users
    sql: SELECT user_id, timestamp FROM events
    measures:
      - name: weekly_active
        sql: id
        type: count_distinct
        rolling_window:
          trailing: 7 day
          offset: start
    dimensions:
      - name: time
        type: time
        sql: timestamp

Data Access Control

Cube's semantic layer coordinates access control upstream of data applications—so that only the right people have access to the right metrics.

In Cube, authorization is based on the security context. Authentication tokens are generated based on your API secret. More about Data Access Control

Caching

Cube’s caching layer ensures that every downstream app stays updated with the latest information—cost-effectively and with low latency.

Cube provides a two-level caching system: in-memory cache and configurable pre-aggregations. More about Caching

Instant APIs

Integrate with data visualization tools and business intelligence dashboards, and bind to popular front-end frameworks to power custom interfaces.

Cube’s SQL, REST, and GraphQL APIs provide the universal compatibility data engineers and developers need. More about APIs

SQL
REST
GraphQL

SELECT
  date_trunc('day', time) as day,
  MEASURE(weeklyActive)
FROM ActiveUsers
WHERE time >= '2021-01-01' AND time < '2021-01-07'
GROUP BY day

What are the use cases of a Semantic Layer?

The use cases of a semantic layer span from unifying your organization’s knowledge base to wildly improving application performance, from cutting down app development time to creating beautiful, data-rich UX/UI—and then some.

Embedded Analytics

Build consistent, secure, and performant embedded analytics.

Semantic Layer

Define metrics upstream to inform every app with the same data.

Real-time Analytics

Trust your real-time data with a stack designed for consistency and speed.

Customer stories

Cloud Academy

With Cube, we’ve been able to speed up time to release a new data model to production by 5x and decrease analytics downtime by 90%.

Alessandro Lollo

Senior Data Engineer at Cloud Academy

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Cyndx

Cube can do whatever your engineers want. They can build your analytics faster. There’s just not another tool out there that would fill in the blank for what we need.HTTP layer and abstraction over SQL.

Sebastian Okser

CTO at Cyndx

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Cuboh

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

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Jobber

Cube really stood out as a great fit for our use case. We were able to level‑up our data infrastructure without needing to build a full‑blown and expensive data pipeline.

Jc Weinrich

Architect in Jobber

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COTA

The Cube platform reduces our development time significantly and integrates easily with data from various sources. The Cube team is collaborative and quick to respond to our requests and their open source ethos keeps things transparent.

Sudhakar Velamoor

VP of Engineering at COTA

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ShopBack

Cube provides us with the framework and tools to build our custom data visualization platform with minimal effort. Our full-stack developers are able to get productive quickly, optimizing performance wherever possible.