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lightdash deploypushes your dbt project to Lightdash

Once you’ve made updates to your dbt project and you’re ready to make those changes available in your Lightdash production project, you have a few options for deploying from the CLI or the Lightdash UI. This guide will walk you through how it works, when to use each option, and what to keep in mind for a safe and scalable workflow.

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Before you deploy: A quick word of caution

Running lightdash deploy uses your local dbt profile (from your profiles.yml). If your local profile isn’t pointing to production, you could deploy to the wrong environment. This works in simple setups, but can introduce risk when using multiple environments or working in a team.
For a safer, scalable approach to deploying and previewing changes, use CI/CD. This allows you to:
  • Spin up Lightdash preview environments for each PR
  • Deploy production changes consistently using shared, secure credentials
  • Avoid accidentally pointing to the wrong target in your local profile
Check out our guide to setting up CI/CD workflow

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Option 1: Deploy via the CLI

Basic usage: 
git checkout main # checkout main or master - or whatever your production branch name is where you've merged your changes
git pull
lightdash deploy # --target prod. If you use developer profiles in your dbt project, you might need this flag. See below.
By default, this will:
  • Use your local dbt profile for the connection
  • Push the current state of your local dbt project files (including uncommitted changes or code from your active branch) to the authenticated Lightdash project
  • Trigger a re-compile and refresh of your Lightdash project
If your dbt project uses different profiles or targets for dev and prod, you can specify which profile to use: 
lightdash deploy --profile prod
Be careful — if your local prod profile isn’t properly pointing to your production database, this can result in incorrect connections (e.g. accidentally deploying to a dev dataset).

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Option 2: Safely refresh metadata using lightdash refresh

If you only need to refresh your Lightdash project to reflect upstream dbt changes (like updated models or docs), but don’t want to deploy your local dbt setup, you can use: 
lightdash refresh
Unlike deploy, this:
  • Uses the saved dbt credentials from Lightdash, not your local profile
  • Simply re-runs the metadata sync and model parsing
  • Makes it safe to trigger from anywhere without risk of pointing to the wrong target
You can also do this directly from the Lightdash UI: Go to your project → “Manage project” → Refresh dbt. This gives the same result as running lightdash refresh.