developing-in-lightdash

name	developing-in-lightdash
description	Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse, apps create/preview/validate), or managing charts, dashboards, spaces and access, AI agents, scheduled content, data apps, data-app external connections, users, groups, custom roles, metrics, and dimensions as code

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
• Downloading, uploading, or locally developing data apps (enterprise)

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

Task	Commands	References
Create a pure Lightdash project from warehouse metadata	Use Lightdash or an already-authenticated warehouse CLI to inspect catalog metadata and aggregate profiles	Creating from a Warehouse Catalog
Discover warehouse tables and fields	lightdash warehouse-catalog --json	CLI Reference
Explore data warehouse values	lightdash sql to execute raw sql, read .csv results	CLI Reference
Define metrics & dimensions	Edit dbt YAML or Lightdash YAML	Metrics, Dimensions
Create charts	lightdash download, edit YAML, lightdash upload	Chart Types
Add period comparisons	Add PoP additional metrics to chart YAML	Period over Period
Build dashboards	lightdash download, edit YAML, lightdash upload	Dashboard Reference
Manage content as code across project and organization resources	lightdash download, lightdash upload	Content as Code
Manage data apps as code (enterprise)	lightdash download --apps <ref> (one app) or --include-apps (all), edit bundle, lightdash upload --apps <ref>; local dev via lightdash apps create/preview/validate	Data Apps, Content as Code
Manage data-app external connections (enterprise)	lightdash download --include-external-connections, edit YAML, lightdash upload	Content as Code
Lint yaml files	lightdash lint	CLI Reference
Set warehouse connection	lightdash set-warehouse from profiles.yml	CLI Reference
Deploy changes	lightdash deploy (semantic layer), lightdash upload (content)	CLI Reference
Test changes	lightdash preview	Workflows

Common Mistakes

Mistake	Consequence	Prevention
Guessing filter values	Case mismatches ('Payment' vs 'payment') cause charts to silently return no data	Always run lightdash sql "SELECT DISTINCT column FROM table LIMIT 50" -o values.csv and use exact values
Not updating dashboard tiles after renaming a chart	Dashboard tile still shows old title — title and chartName are independent overrides that do NOT auto-update	Download the dashboard, find tiles with matching chartSlug, update title and chartName to match
Including unused dimensions in metricQuery	"Results may be incorrect" warning — extra dimensions change SQL grouping and produce wrong numbers	Every dimension in metricQuery.dimensions must appear in the chart config. For cartesian: layout.xField, layout.yField, or pivotConfig.columns
Unsorted YAML keys	lightdash upload warns "unsorted YAML keys" and diffs become noisy	Always sort keys alphabetically at every nesting level — the CLI writes with sortKeys: true
Deploying to wrong project	Overwrites production content	Always run lightdash config get-project before deploying
Missing contentType field	Content type can't be determined without relying on directory structure	Always include contentType: chart, contentType: dashboard, or contentType: sql_chart at the top level
Adding --include-apps to an --apps <ref> selection	--include-apps always requests ALL project apps (capped at 50), so the command downloads every app plus the ref — not just the one app	--apps <ref> alone downloads/uploads only that app (by slug, app URL, or UUID). Use --include-apps only when you want every app

Before You Start

When a task uses lightdash download or lightdash upload, especially for bulk edits, spaces and access, scheduled content, AI agents, data apps, external connections, 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.

Type	Detection	Key Difference
dbt Project	Has dbt_project.yml	Metadata nested under meta:
dbt Fusion / dbt 1.10+	Has dbt_project.yml, uses dbt Fusion or dbt >= 1.10	Metadata nested under config: meta:
Pure Lightdash	Has lightdash.config.yml, no dbt	Top-level properties

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

Working with Data Apps (Enterprise)

Data apps are multi-file React bundles under apps/<app-folder>/ with a lightdash-app.yml manifest — not single YAML files. Full flag semantics and manifest details: Content as Code.

Download one app — --apps <ref> alone is the complete command (ref = slug, app URL, or UUID; also finds apps not added to any space):

lightdash download --apps revenue-explorer --path ./lightdash

Never add --include-apps to "scope" the download — it always requests ALL project apps (see Common Mistakes).

Download all apps: lightdash download --include-apps (capped at 50; raise with --apps-limit <n>). Add --apps-only to skip charts, dashboards, and spaces.

Upload:

lightdash upload --apps revenue-explorer   # one app (slug = folder name, URL, or UUID)
lightdash upload --include-apps            # every app folder on disk

• --app-space <spaceRef> — space (slug or UUID) for apps this upload creates; existing apps keep their space.
• --create-new — create a fresh app (new slug) instead of updating the app referenced in lightdash-app.yml.
• --allow-custom-dependencies — required for non-interactive uploads of apps that declare custom npm dependencies.

Develop locally with the lightdash apps subcommand group:

lightdash apps create "Revenue Explorer"   # scaffold a new app under ./lightdash/apps/
lightdash apps preview                     # run the app locally against your real Lightdash instance, authenticated as you
lightdash apps validate                    # check source, manifest, dependencies, and semantic-layer references

Every created or downloaded app bundle ships its own skills in .claude/skills/ inside the app folder:

• developing-data-apps-locally — the edit → validate → upload loop, local preview, SDK-only data access, and dependency boundaries
• lightdash-data-app — the @lightdash/query-sdk reference for the app's source code

When editing files inside an app folder, read those bundled skills first. They are version-matched to the app and authoritative for local development — this skill only covers moving apps between disk and Lightdash.

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

Command	Purpose
lightdash deploy	Sync semantic layer (metrics, dimensions)
lightdash upload	Upload charts/dashboards
lightdash download	Download charts/dashboards as YAML
lightdash lint	Validate YAML locally
lightdash preview	Create temporary test project
lightdash warehouse-catalog --json	Discover raw warehouse tables
lightdash sql "..." -o file.csv	Run SQL queries against warehouse
lightdash run-chart -p chart.yml	Execute chart YAML query against warehouse
lightdash apps create <name>	Scaffold a new data app locally (enterprise)
lightdash apps preview	Run a data app locally against your Lightdash instance
lightdash apps validate	Validate data app source, manifest, and semantic references

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

Data Pattern	Recommended Chart	Why
Trends over time	Line or area (cartesian)	Shows continuous change with time on X-axis
Category comparisons	Bar (cartesian)	Easy visual comparison between discrete categories
Part-of-whole relationships	pie or treemap	Shows proportions summing to 100%
Single KPI metric	big_number	Focuses attention on one important value
Conversion stages	funnel	Visualizes drop-off between sequential steps
Progress toward target	gauge	Shows current value relative to goal
Geographic data	map	Plots data points or regions on a map
Flow between categories	sankey	Shows how values move from source to target nodes
Detailed records	table	Displays raw data with sorting and formatting
Advanced custom needs	custom	Full Vega-Lite spec for custom visualizations

Type	Use Case	Reference
cartesian	Bar, line, area, scatter	Cartesian
pie	Parts of whole	Pie
table	Data tables	Table
big_number	KPIs	Big Number
funnel	Conversion funnels	Funnel
gauge	Progress indicators	Gauge
treemap	Hierarchical data	Treemap
map	Geographic data	Map
sankey	Flow diagrams	Sankey
custom	Vega-Lite	Custom Viz

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

Pattern	When to Use
Direct (deploy + upload)	Solo dev, rapid iteration
Preview-First	Team, complex changes
CI/CD	Automated on merge

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