dbt Charts

// Overview

dbt Charts is an open-source language from dbt Labs that describes a full interactive dashboard in a single YAML file. SQL still says what data you want; the YAML layer says how you want to see it. The engine compiles that definition into an interactive board, and the bundled dct command-line tool can also render it to HTML, PDF, PNG, SVG or JSON.

The design goal is reviewability. Because a board is one text file, it is version-controlled, diffable, and can be read in a pull request, which means a change to a dbt model and a change to the chart that reads it can travel on the same branch through the same CI run. dbt itself is optional — dbt Charts works with plain SQL — but boards can call ref() and source() when you do run dbt.

As a data-app builder, dbt Charts sits between a warehouse and a published board. Sixteen chart types ship built in, along with five themes, interactive filters and variables. dbt Labs also runs a hosted service at dbtCharts.com, in public beta, that adds hosting, access control, a visual editor, version history, conversational analytics and permissioned sharing on top of the same open language.

// What it does

• A declarative YAML syntax that defines queries, charts and layout in one file
• Sixteen built-in chart types, plus composed charts built from them, and five themes
• The dct CLI validates a board without warehouse access and serves a live preview with working filters
• Renders boards to HTML, PDF, PNG, SVG or JSON for sharing and scheduled reports
• Optional dbt integration: boards can reference existing models through ref() and source()
• Warehouse support for DuckDB, SQLite, Postgres, Snowflake, BigQuery, Redshift, Databricks, Spark and Trino
• dct impact lists the boards a column change would affect before you make it

// Getting started

dbt Charts needs Python 3.10 or newer. Install the dct CLI, point it at a board, and preview it locally.

uv tool install dbt-charts

# or with pip
pip install dbt-charts

# with a warehouse adapter
uv tool install "dbt-charts[snowflake]"

dct --version

dct validate

dct serve

dct render my_board --format html
dct render my_board --format pdf

dct skills intro

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Keep dashboards in the same repo as your dbt models so a model change and its charts ship on one branch
• Review a dashboard change as a readable diff in a pull request instead of an audit log of UI clicks
• Ask a coding agent for a dashboard and get one auditable YAML file back rather than a pile of HTML, CSS and JavaScript
• Render scheduled PDF or PNG reports from the same board definition that serves the interactive version
• Run dct impact before changing a column to see which boards would break

// How dbt Charts compares

dbt Charts alongside other open-source data app builders tools AI/TLDR tracks, ranked by GitHub stars.