AI for data is here, and the long-promised self-serve analytics is finally happening. Anyone with a data connection can chat a report into existence in an afternoon, and the first results are impressive.

The frictions show up fast, though. By default an agent turns one simple report into a pile of files: HTML, CSS, and JavaScript, a couple of chart libraries, and a React or Streamlit app once it has to be live. Tracing a result back to its source means following it through several languages and files, which is slow for people to audit and costs the agent time and tokens on every change.

BI tools went the other way and bolted copilots onto their UI-first apps. That keeps the AI on governed rails, but narrow ones: the agent can do only what the UI exposes.

So today you choose between the messy freedom of code and the narrow control of a BI tool. We built a third option: skip ahead, or read on for how BI got here.

Unbundling BI

As dbt Labs founder Tristan Handy wrote recently in BI’s Second Unbundling:

When I started in data, BI tools were full-stack. Everything happened inside one product: data ingestion, transformation, compute, caching, semantics, visualization, identity. The BI tool was the data stack. MicroStrategy, Cognos, etc: they’re not just visualization tools, they’re integrated data platforms.

Then the modern data stack happened. From ~2015 to 2022, the infrastructure layers of that BI bundle got pulled out and turned into purpose-built infrastructure. Compute went to the Big 5. Ingestion went to Fivetran. Transformation went to dbt. The BI tool was left with: visualization, interactive analytical interfaces, semantic definitions (sometimes!), identity and access management, and web hosting.

What that unbundling left behind is the BI tool we know today, and charts are its biggest piece. They stayed in the UI for good reason: for most people, clicking is quicker than writing YAML. But more and more charts won’t be made by people. As the front end and user of everything becomes increasingly a chat agent, this preference flips. Agents are fluent in code, SQL, and Git, and clumsy in someone else’s UI. So charts need to move to where agents work: into code.

Today we’re taking the next step in unbundling BI: we’re open sourcing dbt Charts, which takes charts out of the BI tool and puts them in code, specifically a new structured YAML language that can declare a full interactive dashboard in one auditable YAML file. Chat freely with an agent, and what it makes has the freedom of code while staying easy to read.

In dbt Charts, SQL remains the language for declaring WHAT data you want to see, and we wrap that in YAML to declare HOW you want to see it.

We’ve spent a long time distilling the language to a few core, extensible elements: deep in what they can express, easy to organize and read. The YAML wraps more than SQL. Markdown carries the prose, and Jinja, as in dbt, carries variables and macros.

Here’s a small example: one variable (a UI filter), one query and one chart.

variables:
  status:
    column: main.documents.status

queries:
  doc_growth: |
    SELECT DATE_TRUNC('month', created_at) AS month,
           SUM(COUNT(*)) OVER (ORDER BY month)
             AS num_docs
    FROM main.documents
    WHERE {{ filter('status', status) }}
    GROUP BY 1

charts:
  growth:
    title: Documents created, all time
    type: area
    query: doc_growth
    x: month
    y: num_docs

rows:
  - growth

That file is the whole board. The CLI renders any board file to static SVG, or to HTML, PNG, PDF, and even the terminal, on your laptop or in CI, and serves a folder of them as a site:

dct render charts/documents.yml --format svg   # or html, png, pdf, terminal
dct serve

Those few elements go deep: over 1,100 config options today, across sixteen chart types and the composed charts built from them. And like any good language, it can express complex layouts and visuals.

You rarely set those options by hand. Styles cascade: a chart inherits from its board, the board from its theme, and a theme is one line to switch. A board can also extends: another board, so a house style or a standard report is written once and inherited everywhere. Boards stay short, and theming stays cheap.

A complete dbt Charts board, rendered from one easy-to-read YAML file.

Deep integration with dbt

You don’t have to use dbt Charts with a dbt project, but when you do, a lot unlocks. The chart layer sits directly on the transform layer, and the deeper the integration, the easier it is to change both.

With dbt Charts, your charts/ directory lives next to your models/ in the same Git repo, so a change to a model and its charts ships on one branch, through one CI run, and breaks before it reaches production.

your_dbt_project/
  .git/
  dbt_project.yml
  models/
  charts/          # new folder in a dbt repo for your dashboards
    revenue.yml

Queries reach models through ref(), resolved from your manifest, so a renamed model or a missing column fails the pull request that broke it, before dbt run rebuilds the warehouse:

dbt parse && dct validate charts/

Support for the dbt Semantic Layer is planned, so a board can use a metric as the project defines it instead of restating its SQL. Follow dbt-labs/dbt-charts#1.

Built for chat

Agents can be quite blind, and they do best with a tight feedback loop. dbt Charts gives them one: strict validation of both the YAML and the SQL, and an extensive set of visualization checks that flag problems before anyone sees the board:

$ dct render charts/revenue.yml
WARN-BAR-BAND-WIDTH-TOO-NARROW
182 bands x 2 series across 640px
Fix: roll up to a coarser grain.

WARN-TABLE-COLUMNS-OVERFLOW
Table needs 980px but only 640px is available.
Fix: drop columns or widen the slot.

A beautiful, cohesive reporting system

We hope dbt Charts, like dbt before it, becomes the open standard language for its layer of the data stack. We designed it for a future where humans and AI build together, and we wanted it to look like that future, not like another dashboard grid. We recruited RJ Andrews, a data graphic designer, author, and historian, to design the charts. His grasp of the craft’s history is what makes the result feel new: it reaches past the dashboard era to what charts looked like when people drew them with care.

Many tools cheat with cards and boxes that fake alignment at the cost of visual noise and lost space. We worked out the spacing, sizing, and layout of every chart, on its own and next to its neighbors.

The result is a cohesive system of charts that feels a level above current BI.

Alongside the open-source language, today we’re launching dbtCharts.com in public beta: a hosted platform for the rest of BI. With charts pulled out, what remains is chiefly hosting, access control, and a UI. By their nature these perhaps can’t be unbundled, or at least shouldn’t be, so the platform handles them on top of the open-source language.

The platform connects to your warehouse and adds conversational analytics, a visual editor for the finishing touches, version history, and sharing with permissions for users and groups, so the people reading a board don’t need a warehouse login.

And of course, these charts were built for chat. The platform has first-class conversational analytics: like Claude or ChatGPT, but with permissioned read-only access to your warehouse and an expert analyst’s skills and tools built in. Explore by chatting with charts, and at any point click in to fine-tune and save the board.

Because it’s built on the open language, every change, from chat, the visual editor, or code, lands in the same YAML in your Git repo. Nothing is locked in: the same board runs on your laptop, in CI, and on the platform, and teams can self-serve, agent in hand, without creating a second, hidden data stack.

Try the beta

The dbt Charts language is open source under the Apache 2.0 license, and you can author, render, and serve boards locally without creating an account. Install it yourself, or hand your coding agent one line:

dbt Charts is pre-1.0 and still changing. When the grammar changes, boards migrate as they parse, so the boards you write today keep rendering. Try it, tell us what is missing^[1][2], join the discussion in #dbt-charts on Slack, and help us build the chart layer that open data infrastructure has been waiting for.