dbt integration
Most data teams already define their business metrics in dbt. The moment a second tool keeps its own copy of "revenue" or "active customer", the numbers start to drift. Verity reads your dbt semantic layer directly, so there is only one definition, and it lives in your repo.
Your semantic models, metrics, and dimensions show up in Verity as a read-only catalog. Data Chat, dashboards, and reports compute with those definitions. A metric you remove in dbt stops being available in Verity, instead of quietly living on as a stale copy.
Changes reach Verity through Git: merge a change to a metric in dbt, and Verity picks it up automatically. No dbt Cloud API or Semantic Layer subscription is needed.
What you get
One definition of every metric
dbt metrics as the source of truth
MetricFlow semantic models, metrics, and dimensions become Verity’s catalog. Nobody in Verity can edit them or compute around them.
You choose the layer per source
Use dbt definitions only, Verity’s own semantic layer only, or both, with dbt taking precedence where they overlap.
Stays in sync with your repo
Verity follows your default branch. A merged change to a metric is live in Verity shortly after, with no manual export.
Column docs come along
Descriptions from your schema.yml files are offered as column descriptions, so business users see the context your analysts wrote.
No warehouse credentials in CI
A small GitHub Actions step parses your project without connecting to the warehouse. Verity reads the result, not your data.
Know what a change breaks (planned)
On a dbt pull request, Verity reports which dashboards and monitors use the models you are changing, right in the PR.
Setup
Connect dbt
1
Install the Verity GitHub App
Give it access to the repository that holds your dbt project. Verity only needs to read it.
2
Add the parse step to GitHub Actions
A short workflow runs dbt parse on every change to your default branch. It needs no warehouse connection.
3
Pick the layer per source
Choose whether a BigQuery source answers with dbt definitions, Verity definitions, or both.
4
Ask with your own definitions
Data Chat, dashboards, and reports now use the metrics your data team defined in dbt.
Ask Verity
Questions you can ask
What was net revenue by channel last month, using our dbt definition?
Which metrics in our dbt project are used on the weekly board dashboard?
How did active customers develop per country this quarter?
Compare gross margin this month with the same month last year.
Want dbt in Verity?
Tell us about your stack. We prioritise new connectors by what our customers run on.