Google Looker

Looker's defining characteristic is its semantic layer, and understanding it explains both the product's strength and its cost of adoption. Business logic is defined once in LookML, sitting directly above the cloud data warehouse and translating SQL into business language. That definition then becomes the single source of truth for metrics regardless of what consumes them, whether a chart, a dashboard or an AI agent. The upfront modelling work is real, and it is the reason metric definitions stay consistent across an organisation instead of drifting between reports.

That backbone is what Google now builds agentic capability on. Conversational Analytics, powered by Gemini, turns dashboards into workspaces where governed agents interpret data and execute on it rather than only displaying it, allowing a business user to move from a natural language question to an automated decision without waiting for an analyst. Dashboard Agents can be deployed inside BI canvases to give AI-powered summaries and deep dives. Google makes an explicit technical argument here: because the semantic layer defines metrics for large language models as well as humans, it eliminates agent hallucinations and keeps results audit-ready.

The platform is positioned as composable and open, combining foundational AI, cloud-first infrastructure, industry leading APIs and the flexible semantic model. Self-service BI and embedded analytics are both supported, the latter being a long-standing Looker strength for putting analytics inside customer-facing products. Google notes recognition as a Leader in the 2025 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. Product documentation, release notes and training are published openly. Looker-specific plan pricing was not retrievable, so confirm it directly.