developing-in-lightdash

Developing in Lightdash

Build and deploy Lightdash analytics projects. This skill covers the semantic layer (metrics, dimensions, joins) and content (charts, dashboards).

When to Use

• Working with Lightdash YAML files (charts, dashboards, models as code)
• Using the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql)
• Defining metrics, dimensions, joins, or tables in dbt or pure Lightdash projects
• Creating or editing charts and dashboards as code

Don't use for: Developing the Lightdash application itself (use the codebase CLAUDE.md), general dbt work without Lightdash metadata, or raw SQL unrelated to Lightdash models.

What You Can Do

Common Mistakes

Before You Start

When a task uses lightdash download or lightdash upload, especially for bulk edits, spaces and access, scheduled content, AI agents, users, groups, or custom roles, read and follow Content as Code first. Project and organization content require separate commands, and a default download is not a complete snapshot.

Check Your Target Project

Always verify which project you're deploying to. Deploying to the wrong project can overwrite production content.

lightdash config get-project        # Show current project
lightdash config list-projects      # List available projects
lightdash config set-project --name "My Project"  # Switch project

Detect Your Project Type

The YAML syntax differs significantly between project types.

ls dbt_project.yml 2>/dev/null && echo "dbt project" || echo "Not dbt"
ls lightdash.config.yml 2>/dev/null && echo "Pure Lightdash" || echo "Not pure Lightdash"

dbt Fusion / dbt 1.10+: Lightdash metadata must be nested under config: meta: instead of meta:. The properties are identical — only the nesting changes. Example:

models:
  - name: orders
    config:
      meta:
        metrics:
          total_revenue:
            type: sum
            sql: "${TABLE}.amount"

Syntax Comparison

dbt YAML (metadata under meta:):

models:
  - name: orders
    meta:
      metrics:
        total_revenue:
          type: sum
          sql: "${TABLE}.amount"
    columns:
      - name: status
        meta:
          dimension:
            type: string

Pure Lightdash YAML (top-level):

type: model
name: orders
sql_from: 'DB.SCHEMA.ORDERS'

metrics:
  total_revenue:
    type: sum
    sql: ${TABLE}.amount

dimensions:
  - name: status
    sql: ${TABLE}.STATUS
    type: string

Setting Up Warehouse Connection

If the project needs a different warehouse connection (e.g., switching from Postgres to BigQuery), update it from your profiles.yml:

lightdash set-warehouse --project-dir ./dbt --profiles-dir ./profiles --assume-yes

This reads credentials from profiles.yml, updates the warehouse connection on the currently selected project, and triggers a recompile. Run this before lightdash deploy.

To target a specific project:

lightdash set-warehouse --project-dir ./dbt --profiles-dir ./profiles --project <uuid> --assume-yes

Core Workflows

Verify Filter Values Before Using Them

CRITICAL: Never guess filter values. Case mismatches (e.g., 'Payment' vs 'payment') cause charts to silently return no data.

Filters are case-sensitive by default. The case_sensitive key can override this in order of priority:

• Dimension metadata
• Model/explore metadata
• lightdash.config.yml defaults.case_sensitive

Before writing any string filter, query actual values from the warehouse:

lightdash sql "SELECT DISTINCT category FROM payments LIMIT 50" -o category_values.csv

Read the CSV and use the exact values in your filter YAML. This applies to all equals/notEquals filters with string values — in charts and dashboards.

Editing Metrics & Dimensions

1. Find the model YAML file (dbt: models/*.yml, pure Lightdash: lightdash/models/*.yml)
2. Edit metrics/dimensions using the appropriate syntax for your project type
3. Validate: lightdash lint (pure Lightdash) or dbt compile (dbt projects)
4. Deploy: lightdash deploy

See Metrics Reference and Dimensions Reference for configuration options.

Creating a Pure Lightdash Project from a Warehouse Catalog

When the prepared project has no usable dbt project and the task is to bootstrap a semantic layer from warehouse metadata, always read and follow Creating from a Warehouse Catalog before inspecting data or writing YAML. This applies whether warehouse access comes from the selected Lightdash project or an already-authenticated warehouse CLI such as Snowflake CLI or bq. Do not use that workflow when an existing dbt semantic layer can be extended.

Editing Charts

1. Download: lightdash download --charts chart-slug
2. Edit the YAML file in lightdash/ directory
3. Verify filter values: If you added or changed filters, use lightdash sql to check actual column values (see Common Mistakes)
4. Update dashboard tiles: If you changed the chart's name or purpose, download any dashboards that reference it and update their tile title and chartName properties to match (see Common Mistakes)
5. Lint: lightdash lint to validate before uploading
6. Upload: lightdash upload --charts chart-slug (and any modified dashboards)

Dashboard tiles have their own titles. A saved_chart tile's title and chartName properties are independent overrides — they do NOT auto-update when you rename the chart. If you change a chart from "Total Revenue" to "Gross Profit" but don't update the dashboard tile, the dashboard will still display "Total Revenue". Always download the dashboard, find tiles with matching chartSlug, and update their title and chartName to match.

# Dashboard tile — title and chartName must be updated manually when chart changes
tiles:
  - type: saved_chart
    properties:
      chartSlug: total-revenue-kpi
      title: "Gross Profit"        # ← Update this when chart name/purpose changes
      chartName: "Gross Profit"    # ← Update this too

Editing Dashboards

1. Download: lightdash download --dashboards dashboard-slug
2. Edit the YAML file in lightdash/ directory
3. Verify filter values: If you added or changed filters, use lightdash sql to check actual column values (see Common Mistakes)
4. Lint: lightdash lint to validate before uploading
5. Upload: lightdash upload --dashboards dashboard-slug

Creating New Content

Charts and dashboards are typically created in the UI first, then managed as code:

1. Create in UI
2. lightdash download to pull as YAML
3. Edit and version control
4. lightdash lint to validate before uploading
5. lightdash upload to sync changes

Testing with Preview

For larger changes, test in isolation:

lightdash preview --name "my-feature"
# Make changes and iterate
lightdash stop-preview --name "my-feature"

CLI Quick Reference

See CLI Reference for full command documentation.

Semantic Layer

The semantic layer defines your data model. See individual references for full configuration:

• Tables Reference — queryable entities, labels, joins
• Metrics Reference — aggregated calculations (count, sum, average, min, max, number, etc.)
• Dimensions Reference — attributes for grouping/filtering (string, number, boolean, date, timestamp)
• Joins Reference — cross-table relationships
• User Attributes Reference — SQL variables, row-level security, access control

Chart Types

All charts share a common base structure:

chartConfig:
  config: {}        # Type-specific — see individual references
  type: <type>
contentType: chart              # Required: chart, dashboard, or sql_chart
dashboardSlug: my-dashboard  # Optional: scopes chart to dashboard (won't appear in space)
metricQuery:
  dimensions:
    - my_explore_category
  exploreName: my_explore     # Required: which explore to query
  filters: {}
  limit: 500
  metrics:
    - my_explore_total_sales
  sorts: []
name: "Chart Name"
slug: unique-chart-slug
spaceSlug: target-space
tableConfig:
  columnOrder: []
tableName: my_explore           # Required: top-level explore/table name
version: 1

Key ordering: All YAML keys must be sorted alphabetically at every nesting level. The CLI writes files with sortKeys: true and warns on upload if keys are unsorted. When writing or editing YAML by hand, keep keys in alphabetical order to avoid warnings and noisy diffs.

Chart scoping: Use spaceSlug only for shared charts. Add dashboardSlug to scope a chart to a specific dashboard (it won't appear in the space).

Nested spaces: Spaces can be nested. In YAML, spaceSlug uses parent/child syntax to address a sub-space — the / denotes hierarchy. Examples:

spaceSlug: sales              # Top-level space "sales"
spaceSlug: sales/maps         # Sub-space "maps" inside "sales"
spaceSlug: sales/eu/forecasts # Deeper nesting works the same way

Each path segment must be the slug of an existing (or to-be-created) space at that level. A bare slug like sales-maps is a flat top-level space, NOT a sub-space — the slash is the only thing that creates the hierarchy.

Choosing the Right Chart Type

Dashboards

Dashboards arrange charts and content in a grid layout. See Dashboard Reference for YAML structure, tile types, tabs, and filters.

Exploring the Warehouse

Use lightdash sql to explore data when building models:

# Preview table structure
lightdash sql "SELECT * FROM orders LIMIT 5" -o preview.csv

# Check distinct values for a dimension
lightdash sql "SELECT DISTINCT status FROM orders" -o statuses.csv

# Test metric calculations
lightdash sql "SELECT SUM(amount) FROM orders" -o test.csv

Workflow Patterns

See Workflows Reference for detailed examples and CI/CD configurations.

Resources

Semantic Layer

• Creating from a Warehouse Catalog
• Dimensions Reference
• Metrics Reference
• Tables Reference
• Joins Reference
• User Attributes Reference

Charts

• Cartesian Chart Reference - Bar, line, area, scatter
• Pie Chart Reference
• Table Chart Reference
• Big Number Reference
• Funnel Chart Reference
• Gauge Chart Reference
• Treemap Chart Reference
• Map Chart Reference
• Sankey Chart Reference
• Custom Viz Reference
• Period over Period Reference - PoP comparisons (YoY, MoM, etc.)

Dashboards & Workflows

• Dashboard Reference
• Dashboard Best Practices
• Content as Code Reference
• CLI Reference
• Workflows Reference

External

• Lightdash Docs