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Lightdash works with the tools your team already uses. Connect a dbt project to build your semantic layer from it, send chart results to Slack and Google Sheets, deliver alerts and scheduled reports, query your semantic layer from any SQL client, and operate Lightdash agentically from Snowflake Cortex. One closely related integration is documented in its own product area: the Lightdash MCP server, which lets AI assistants explore your data and run queries directly.

Connect your warehouse and project

Connect your warehouse and choose a dbt or native Lightdash YAML project

Connect through an SSH tunnel

Reach a Postgres or Redshift database in a private VPC by tunnelling through a bastion host

Connect Athena with web identity

Connect to Amazon Athena without storing AWS keys, using an IAM role that trusts Lightdash

Slack integration

Unfurl chart links, deliver scheduled reports and alerts, and chat with your AI agents in Slack

Metrics SQL API

Query your Lightdash semantic layer over the Postgres wire protocol from any SQL client or BI tool

Snowflake Cortex

Operate your BI layer agentically by pairing Lightdash with Snowflake's Cortex Code agent

Apache Ossie

Exchange semantic models with other tools using the Apache Ossie open specification

dbt

Syncing dbt projects

Sync your dbt project to Lightdash by deploying with the CLI or refreshing from a Git connection

dbt write-back

Develop models and metrics in Lightdash and open a pull request against your dbt project

dbt MetricFlow metrics

Connect Lightdash to your dbt MetricFlow semantic layer metrics

Upgrading dbt

What changes in your Lightdash setup when you move between dbt versions

Google Sheets

Lightdash for Google Sheets™

Import saved charts or build refreshable Google Sheets reports with sorting, totals, and row groups