Semantic Layer

The semantic layer defines business-friendly models over lakehouse tables. It gives teams and AI agents shared names for measures, dimensions, timestamps, filters, and relationships so the same metric is computed the same way everywhere.

Use semantic models when analysts, dashboards, and Ask AI should work with concepts like monthly recurring revenue, active accounts, or signup cohort instead of rewriting raw SQL each time.

What a model contains

A semantic model is attached to a table or view and can define:

• Measures: aggregations such as revenue, active users, order count, or average latency.
• Dimensions: fields used for grouping and filtering, such as plan, region, customer tier, or status.
• Timestamps: time fields used for trends and time grains.
• Relations: joins to other models so analyses can cross business entities.
• Formatting: display hints such as currency, percentages, and date formats.

Where models are used

Semantic models power semantic insights, Ask AI, tasks, and connected AI clients. They also make data discovery easier because important tables carry explicit business meaning.

Learn more

• Insights: build semantic and SQL analyses.
• Ask AI: ask natural-language questions over models and data.
• Knowledge entries: add supporting business documentation.