Post Thread

dbt Charts introduces an open-source, declarative approach that treats dashboards as code—living alongside dbt models so they can be version-controlled, reviewed in pull requests, and validated just like data transformations. By combining #SQL and YAML, humans and AI agents can collaboratively build, modify, and ship trustworthy visualizations that stay in sync when underlying models change. This gives AI the governed context it needs to produce reliable answers while extending analytics engineering practices all the way through the BI layer.

Sample chart definition using #SQL and YAML:

queries:
  revenue_by_month:
    #sql: |
      SELECT date_trunc('month', ordered_at) AS month,
             sum(product_price) AS revenue
      FROM MY_DATABASE.MY_SCHEMA.order_items
      GROUP BY 1
      ORDER BY 1

charts:
  revenue_trend:
    type: line
    query: revenue_by_month
    x: month
    y: revenue
    title: "Revenue by month"

https://www.getdbt.com/resources/webinars/introducing-dbt-charts-dashboards-as-code