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AI-powered analytics platform that empowers users to get instant insights from data using natural language search and AI-driven automation.

The open-source BI platform that turns your dbt project into a governed, version-controlled analytics engine.

Lightdash is a modern business intelligence platform designed specifically for teams using dbt (data build tool). By leveraging the dbt semantic layer, Lightdash ensures that metrics and dimensions defined in the data warehouse are automatically reflected in the BI layer, eliminating the 'metric drift' common in legacy tools like Tableau or Power BI. As of 2026, Lightdash has positioned itself as the leading alternative to Looker, offering a Git-integrated workflow where visualizations and dashboards are defined as code. Its architecture is built around the concept of a single source of truth, allowing data engineers to govern data models in YAML while empowering end-users to explore data through a drag-and-drop interface. The 2026 market position highlights its deep integration with the Modern Data Stack (Snowflake, BigQuery, Databricks) and its ability to provide high-trust analytics with significantly lower overhead than traditional enterprise BI suites. Its open-source core allows for extensive customization, while its managed cloud service provides SOC2-compliant, high-availability infrastructure for scaling enterprises.
Lightdash is a modern business intelligence platform designed specifically for teams using dbt (data build tool).
Explore all tools that specialize in automated dashboard generation. This domain focus ensures Lightdash delivers optimized results for this specific requirement.
Native integration with dbt Semantic Layer (MetricFlow), allowing users to query metrics directly defined in dbt.
Automatically generates temporary BI environments for GitHub Pull Requests.
A direct SQL interface within the BI tool for ad-hoc analysis that bypasses the semantic layer when needed.
Client-side calculation engine for post-processing data without writing additional SQL.
Visualizes the flow of data from warehouse source tables to specific dashboard tiles.
Export dashboard and chart definitions into YAML files for version control.
Anomaly detection and threshold-based alerts delivered via Slack or Webhook.
Connect your data warehouse (Snowflake, BigQuery, Redshift, etc.) to a dbt project.
Install the Lightdash CLI locally using npm or yarn.
Authenticate the CLI with your Lightdash Cloud or Self-hosted instance.
Define your dimensions and metrics in your dbt project's .yml files.
Run 'lightdash deploy' to sync your dbt schema and documentation with Lightdash.
Navigate to the Lightdash UI to verify the connection and project structure.
Create your first 'Explore' by selecting a dbt model and dragging fields onto the canvas.
Save visualizations as 'Charts' and group them into 'Dashboards'.
Configure Role-Based Access Control (RBAC) for your team members.
Set up scheduled Slack or Email deliveries for key performance indicators.
All Set
Ready to go
Verified feedback from other users.
"Users praise the tight integration with dbt and the 'BI-as-code' philosophy, though some find the initial setup steeper than plug-and-play tools."
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