Looker Conversational Analytics
by Google Cloud
Chat with governed Looker data, powered by Gemini
Reviewed by the the30top research team · Last reviewed 2026-10-27
Overview
Conversational Analytics in Looker is a Gemini-powered chat feature grounded in Looker's semantic layer, letting users query Explores and dashboards through data agents and run advanced analytics that translate questions into Python. It supports BigQuery, AlloyDB, Redshift, Snowflake and Databricks as data sources. Google documents real limits: some features are in preview, queries return at most 50,000 rows, answers are suited to a single visualization, and prediction, forecasting and advanced statistics such as correlation and anomaly detection are not yet supported.
Our verdict
Looker's conversational analytics is a natural upgrade for teams with a mature LookML model, with governance built in. Preview status and documented limits mean it is not yet a full replacement for an analyst.
Best for
Organizations with a Looker semantic model that want governed chat on top of it.
Avoid if
You do not use Looker or need forecasting and statistical analysis from chat.
Pros
- Grounded in the Looker semantic layer
- Supports several warehouses
- Usage and token monitoring
Cons
- Several features are in preview
- Answers limited to single visualizations
- No forecasting or advanced statistics yet
Key capabilities
Frequently asked questions
What data sources does it support?
Google's documentation lists BigQuery, AlloyDB, Redshift, Snowflake and Databricks.
What are the main limits?
Queries return at most 50,000 rows, answers suit a single visualization, and forecasting and advanced statistics are not yet supported.
How is it priced?
Google points to its Looker pricing page for Gemini in Looker feature pricing.