Semantic & metrics layers · Malloy Data (a Series of LF Projects, LLC)

Malloy

Open-source modeling and query language that compiles to SQL, letting analysts define reusable metrics and explore data without repeating joins.

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Malloy is an open-source language, created by the team behind Looker's LookML, for describing data relationships and querying them. Instead of writing repetitive SQL joins and aggregations, analysts define a semantic model once — sources, joins, and named measures/dimensions — and then write short, composable queries against it; Malloy compiles both the model and the query into standard SQL that runs on the underlying database. It ships as a VS Code extension, a CLI, and a JavaScript library, and supports BigQuery, Snowflake, DuckDB, MotherDuck, PostgreSQL, MySQL, Trino/Presto, and Databricks. Since 2025, governance of the project moved from Google directly to the Linux Foundation as an independent, vendor-neutral project. Malloy has no first-party hosted product; it is a language and toolchain that teams embed in notebooks, IDEs, or their own applications rather than a semantic-layer service with an API and access controls out of the box.

At a glance

Vendor Malloy Data (a Series of LF Projects, LLC)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Analytics engineers who want reusable, DRY data models without adopting a full semantic-layer service.

Pricing

Malloy is a free, open-source language and toolchain with no vendor-hosted product or pricing page.

Pricing has not been verified yet — see the vendor's site.

Features

  • Reusable semantic models (sources, joins, named measures/dimensions)
  • Compiles to standard SQL for the target database
  • Concise, composable query syntax as a SQL alternative
  • VS Code extension for interactive data exploration
  • CLI and JavaScript library for embedding in applications
  • Broad SQL-engine support (BigQuery, Snowflake, DuckDB, Databricks, and more)

Integrations

Profile last reviewed September 21, 2026

Alternatives

Cube Open-source headless semantic layer that defines metrics once and serves them via SQL, REST, and GraphQL APIs to any BI tool or app. AtScale Universal semantic layer that models metrics once on top of a cloud data warehouse and exposes them to any BI tool without data movement. dbt Transforms data already in the warehouse with SQL SELECT statements, adding version control, testing, documentation and lineage.

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Terms to know

Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis.

Related guides

How to choose a semantic layer Semantic layers differ by where they sit relative to the warehouse and who they serve — one BI tool, every BI tool, an app, or an AI agent.