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Dbt Charts

Dbt Charts is a Python package and CLI that compiles declarative YAML board definitions, with SQL queries specifying the data, into interactive dashboards and reports. Its `dct` CLI supports project initialization, YAML validation, previews, queries and exports to HTML, PDF and PNG.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Dbt Charts — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want SQL to define the data and YAML to specify dashboard structure, with CLI-based validation, previewing or export. For query execution, install the dbt adapter matching your warehouse in the CLI environment; DuckDB is built in.

Who it is for

  • Analytics engineers and data teams defining dashboard layouts as inspectable YAML alongside SQL.
  • Developers who want a command-line workflow for validating, previewing and exporting dashboard boards.
  • Coding agents that can author dashboard configuration for people to inspect.

Documented uses

  • Define dashboard queries in SQL and describe their presentation in YAML.
  • Initialize a chart project and validate board definitions without a warehouse.
  • Preview boards and export dashboards or reports as HTML, PDF or PNG.
  • Keep dashboard specifications in repeatable, reviewable configuration.
Before you choose

Requirements & limits

The package is open source under Apache-2.0. The README labels it beta and pre-1.0 and requires Python 3.11–3.13. It documents both uv and pip installation. The hosted platform is described separately from the CLI.

  • Warehouse queries require the relevant dbt adapter installed in the CLI environment; DuckDB is built in.
  • The CLI can validate YAML without a warehouse, but that is distinct from running queries.
  • The documented CLI export formats are HTML, PDF and PNG.
  • The repository is a read-only mirror: issues are welcome, but pull requests are not accepted.
  • The README identifies the project as beta and pre-1.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Initialize and validate a dashboard project

Create a starter chart project and check its board YAML before connecting to a warehouse.

Before you start

  • Python 3.11–3.13
  • Install the dbt-charts CLI using uv or pip

Steps

  1. Install with the command below or the command below.

    Documented command
    uv tool install dbt-charts
    Documented command
    pip install dbt-charts

  2. Run dct init to create a project, including charts/guide.yml.

  3. Run dct validate charts/guide.yml to check the board YAML; this validation does not require a warehouse.

Expected outcome

A starter project is created and its board YAML can be checked for errors without a warehouse connection.

Guide limitations

  • This workflow documents project setup and YAML validation, not execution of warehouse queries.
  • The project is beta and pre-1.0.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
13 October 2026
Original website title
dbt-labs/dbt-charts · Official homepage title · retained in its source language
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