Metriql

5.5easy start · #25 of 28
in Semantic Layer Software

Free to practise onnot on record
Free trialnot on record
Well documentednot on record
Runs where you worknot on record

Runs on api, self-hosted, Web.

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Metriql is ranked #25 of 28 in semantic layer software on The Geeks Club. It runs on API, Self-hosted, Web.

Compared on semantic layer software

Governed metrics: Yes
Dimensions and joins: Yes
Query API: Yes
Deployment model: self_hosted

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The record

Product: Metriql describes itself as headless BI and an open-source metrics store for defining metrics as code and sharing them across BI and data tools.
dbt integration: Metriql integrates with dbt, maps dbt resources to datasets, and can generate roll-up models.
Central metrics: Metriql lets teams define metrics centrally and synchronize them to other tools.
Query execution: Metriql rewrites queries and runs them on the target database; it says it does not embed Trino or process data itself.
Aggregates: Metriql can programmatically create dbt roll-up models to speed up BI and other data tools.
Interfaces: The project includes a REST API, JDBC driver, and CLI, and supports embedded analytics through the REST API.
BI integrations: The integration list marks Tableau, Redash, Superset, Mode, Metabase, Looker, Google Data Studio, and others as ready; Power BI is in progress and Qlik and MicroStrategy need testing.
Integration maturity: The BI integrations page says all integrations are in alpha.
Database support: The listed adapters include Postgres, Redshift, BigQuery, Snowflake, Presto, MySQL, SQL Server, Azure SQL, and ClickHouse; the page notes that not all dbt adapters are supported.
Security: The REST API supports JWT-based authorization when configured with API auth secrets, and embedded analytics supports authorization and CORS.
Data handling: Metriql's privacy policy says its connectors may cache data in short-lived encrypted caches and that data almost always remains in the user's environment.
Notable limit: Metriql does not run dbt models; users need to run table or incremental models themselves.
Support: The project directs users to its Slack community and welcomes integration suggestions and contributions on GitHub.
Intended users: The docs describe use by business, finance, and marketing teams using BI tools, product managers using analytics tools, analysts using SQL, and data scientists using Python, R, and APIs.

Where it ranks on The Geeks Club

Best Semantic Layer Software in 2026#25 of 28

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Sources

metriql.com· checked 4 Oct 2026
metriql.com/introduction/intro/· checked 4 Oct 2026
metriql.com/integrations/bi-tools/index/· checked 4 Oct 2026
metriql.com/introduction/aggregates/· checked 4 Oct 2026
github.com/metriql/metriql· checked 4 Oct 2026
metriql.com/introduction/supported-adapters/· checked 4 Oct 2026
metriql.com/integrations/rest-api/· checked 4 Oct 2026
metriql.com/privacypolicy/· checked 4 Oct 2026
metriql.com/faq/· checked 4 Oct 2026