Looker

Google Cloud BI platform built around a governed semantic model

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What is Looker?

Looker is Google Cloud's business intelligence platform, and its defining feature is LookML, a modelling language that defines dimensions, measures and relationships once so every downstream report uses the same logic. Analysts write SQL in a modelling layer; business users then explore through a browser interface with consistent numbers. It supports embedded analytics, scheduled delivery, and workflows that push data back into operational systems. AI features under the Gemini umbrella can summarise dashboards, explain metric changes and answer questions in natural language, but they depend entirely on the quality of the LookML model beneath them.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

LookML semantic model that centralises metric and dimension definitions
Browser-based exploration with saved Looks and dashboards
Embedded analytics and an API for customer-facing data products
Scheduled delivery, alerts and data actions that write back to systems
Version control for model code with development and production modes
Gemini-assisted dashboard summaries and natural-language exploration

Pros & cons

Strengths

One governed definition of every metric across the whole company
Model-as-code fits review, testing and version control workflows
Strong embedding story for building analytics into other products

Watch out for

Enterprise pricing and annual commitments put it out of reach for small teams
LookML is a real language that takes time for new analysts to learn
Model changes need review discipline, or downstream reports break

Best for & use cases

governed enterprise bi, metric consistency, embedded analytics and data products

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FAQ

How is Looker different from Looker Studio?

They are separate products. Looker Studio is a free, self-service reporting tool with a simple data-source model. Looker is the enterprise platform with the LookML semantic layer, embedding and governed metric definitions, and it is priced accordingly.

What problem does LookML actually solve?

It puts metric definitions in code so revenue, active users or margin are defined once rather than re-derived in every report. That removes the argument about whose dashboard is correct, but it requires a modelling team to maintain the code.

Do I need the AI features to get value?

No. The semantic model is the reason teams adopt Looker. Gemini features are conveniences layered on top, and they only produce trustworthy answers when the underlying model is well defined and documented.