Lightdash YAML

Use Lightdash without a dbt project by defining your semantic layer directly in YAML files.

What is Lightdash YAML?

Lightdash YAML allows you to use Lightdash without an existing dbt project. Instead of defining your semantic layer within dbt model YAML files, you define it directly in standalone YAML files that point to tables in your data warehouse.

This approach lets you leverage Lightdash’s powerful features without needing to adopt dbt first.

Why use Lightdash YAML?

Lightdash has always operated with dbt at its core. Traditionally, customers have active dbt projects, and Lightdash builds its semantic layer within that dbt context.

However, not every team uses dbt. If you’re interested in Lightdash features like:

• AI agents that can answer questions about your data
• A semantic layer with consistent metric definitions
• Self-service analytics for your business users

…but you don’t have dbt set up, Lightdash YAML provides a path forward. You can define your semantic layer directly and start using Lightdash immediately, without the overhead of adopting dbt.

dbt vs Lightdash YAML: which should you use?

Scenario	Recommendation
You already have a dbt project	Use the standard dbt integration
You’re planning to adopt dbt soon	Consider setting up dbt first, then connecting to Lightdash
You don’t use dbt and want to try Lightdash quickly	Use Lightdash YAML
You want AI agents or semantic layer features without dbt	Use Lightdash YAML
You have tables in your warehouse ready to explore	Use Lightdash YAML

The good news: if you start with Lightdash YAML and later decide to adopt dbt, the YAML formats are compatible, so migration is straightforward.

Getting started with Lightdash YAML

Prerequisites

Before you begin, make sure you have:

1. The Lightdash CLI installed
2. Authenticated with Lightdash
3. Credentials for your data warehouse (e.g., Snowflake)
4. Tables in your warehouse that you want to explore

Step 1: Create your project configuration

At the root of your project, create a lightdash.config.yml file to specify your warehouse type:

warehouse:
  type: snowflake

Replace snowflake with your warehouse type (e.g., bigquery, databricks, redshift, postgres, trino).

Step 2: Create your first model

Lightdash YAML uses the same syntax as the Lightdash semantic layer in dbt, but instead of nesting everything under meta tags, all configuration is at the top level of the file.

Create a directory structure for your Lightdash project:

mkdir -p lightdash/models

Create a YAML file for your first model. For example, ./lightdash/models/users.yml:

# Metadata
type: model
name: users

# Table definition
sql_from: 'DB.SCHEMA.USERS'

# Metric definitions
# For more configuration see: https://docs.lightdash.com/references/metrics
metrics:
  user_count:
    type: count_distinct
    sql: ${TABLE}.USER_ID
    description: Total unique users

# Dimension definitions
# For more configuration see: https://docs.lightdash.com/references/dimensions
dimensions:
  - name: subscription_type
    sql: ${TABLE}.SUBSCRIPTION
    type: string

  - name: signed_up_at
    sql: ${TABLE}.SIGNED_UP
    type: date
    time_intervals:
      - DAY
      - WEEK
      - MONTH

Update the configuration:

• Set sql_from to the fully qualified name of your table (e.g., DATABASE.SCHEMA.TABLE)
• Update the dimensions to match the columns in your table
• Add metrics that make sense for your data

Step 3: Validate your YAML

Check your YAML files for errors by running:

lightdash lint

This command validates your configuration and reports any issues before you deploy.

Step 4: Deploy your project

Create your Lightdash project by deploying with the CLI:

lightdash deploy --create --no-warehouse-credentials

The --no-warehouse-credentials flag tells Lightdash that you’re deploying without warehouse credentials embedded in the CLI. After deploying, you’ll need to configure your warehouse connection in the Lightdash UI:

1. Go to Settings (gear icon in the top right)
2. Under Current project, click Connection settings
3. Configure your Warehouse connection with your database credentials

For detailed instructions on configuring your warehouse connection, see Connect to a warehouse.

Step 5: Update your project

After your initial deployment, you can edit your .yml files and redeploy changes:

lightdash deploy --no-warehouse-credentials

Developing with AI copilots

If you’re developing with Cursor, Claude Code, Kilo Code, or another AI copilot, you can speed up your workflow significantly.

Recommended setup

1. Provide the schema reference: Give your copilot this link to the Lightdash YAML format specification:

   https://raw.githubusercontent.com/lightdash/lightdash/refs/heads/main/packages/common/src/schemas/json/model-as-code-1.0.json
   

2. Use validation: Prompt your copilot to always run lightdash lint after making any changes to catch errors early.

3. Share your warehouse schema: Give your copilot access to your warehouse schema. This makes it very fast to generate YAML files that match your actual table structures.

With access to your warehouse schema, AI copilots can auto-generate dimension and metric definitions for entire tables in seconds.

Next steps

Once you’ve deployed your Lightdash YAML project:

• Explore your data in the Lightdash UI
• Create metrics to define your key business calculations
• Set up AI agents to enable natural language queries
• Learn about the semantic layer reference for advanced configuration options