Looker

Looker is the right BI tool for organizations that value governance and consistency over visual flexibility. If your biggest analytics pain is that the same metric shows different numbers in different dashboards, LookML solves that problem architecturally, not through process discipline.

The BigQuery integration is the tightest in the market. Looker generates SQL that takes advantage of BigQuery's architecture, and the two products share Google's investment in a unified analytics stack. For teams on GCP, Looker is the natural BI layer. Combined with dbt for transformation, you get a two-tier governance model: dbt governs how raw data becomes clean tables, and LookML governs how those tables are presented to business users.

Looker offers embedded analytics, but the per-user licensing makes it expensive for customer-facing deployments at scale. For teams where embedded dashboards are a primary use case, Omni offers a more cost-effective embedding model alongside native dbt integration.

LookML is Looker's defining strength, and it rewards teams who invest in it. Someone on your team needs to learn and maintain the LookML model. For organizations with data engineers or analytics engineers, this is a natural fit. Organizations that want analysts to build their own dashboards with minimal modeling will want to plan for that LookML ownership up front, and we build it into every engagement.

Looker deployments stay healthy when the internal team can extend the LookML model themselves. The risk we guard against is the opposite: an initial LookML project built by an external consultant who then leaves, with no one internal able to extend the model. New business questions arise, and instead of adding a dimension to LookML, analysts write SQL queries in notebooks, defeating the purpose of the governed model. Every Looker engagement we do includes training and documentation so your team can own the model long-term.

Looker's persistent derived tables (PDTs) let you pre-compute expensive queries and serve them as fast tables within the LookML model. Many Looker deployments have slow dashboards because every view runs a complex query against the full dataset. PDTs can reduce query times from minutes to seconds for frequently accessed views.

The Looker API is another underused capability. It supports programmatic access to dashboards, looks, and data, which enables use cases like automated reporting, data distribution to external systems, and integration with custom applications. Teams that treat Looker only as a dashboard tool miss its potential as a data access layer.

Google is integrating Gemini with Looker for natural language querying. Because Looker has a semantic model (LookML), AI-generated queries can reference governed metric definitions rather than raw SQL — a meaningful advantage of the semantic-layer approach. Omni takes a similar semantic-layer-grounded path and has shipped a broad AI feature set, including agentic multi-step analysis, AI filtering, and an MCP server. Looker's AI capabilities are maturing quickly on the same foundation, and both benefit from grounding AI in governed metrics rather than raw SQL.