Every Modern Data Stack Needs a Semantic Layer

Connect data silos, drive consistent metrics, and power your AI and analytics with context.

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.

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 Models to model raw data into meaningful business definitions and pre-aggregate data for optimal results.

More about Data Modeling

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

cube(`active_users`, {
sql: `SELECT user_id, timestamp FROM events`,

measures: {
weekly_active: {
sql: `user_id`,
type: `count_distinct`,
rolling_window: {
trailing: `7 day`,
offset: `start`,
},
},
},

dimensions: {
time: {
sql: `timestamp`,
type: `time`,
},
},
});

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

SELECT
DATE_TRUNC('day', time) AS day,
MEASURE(weekly_active)
FROM active_users
WHERE time >= '2021-01-01' AND time < '2021-01-07'
GROUP BY day

{
"measures": [
"active_users.weekly_active"
],
"timeDimensions": [
{
"dimension": "active_users.time",
"granularity": "day",
"dateRange": [
"2021-01-01",
"2021-01-07"
]
}
]
}

query CubeQuery {
cube {
active_users(
where: {
time: { inDateRange: ["2021-01-01", "2021-01-07"] }
}
) {
weekly_active
time {
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.