Looker

Data that drives decisions

Code-first BI platform with LookML semantic layer for governed, consistent metrics.

Our take

Looker's architecture — LookML defines business logic in a version-controlled semantic layer, dashboards consume that layer — solves the consistency problem that plagues Tableau: every analyst building their own definitions for 'revenue' or 'active users.' LookML forces one authoritative definition for every metric, stored in Git, reviewable in PRs. The dbt Semantic Layer integration (dbt metrics → Looker) extends this further. The cost is the main barrier: Looker starts around $3,000-$5,000/month, which prices out most small and mid-market companies. Google acquisition (2019) brought tight BigQuery integration and Looker Studio (free), but the enterprise Looker platform retained separate pricing. Looker's embedded analytics capability is one of the strongest for companies building white-labeled analytics into SaaS products. Teams choosing Tableau vs Looker: Tableau for visual/drag-and-drop analytics teams, Looker for code-first data engineering teams who want governed metric consistency.

Product roadmap

Looker (under Google) is integrating with Vertex AI for natural language analytics, expanding the Semantic Layer to function as a universal metric definition layer consumed by any tool, and building out Looker Studio Pro for enterprise Looker Studio users who need collaboration and data source governance.