Run dbt inside the
full data workflow

Run our dbt revenue models after the Stripe ingest finishes, then alert analytics if the output changes unusually.

Mage

I've updated the workflow.

  • Stripe data lands first
  • dbt revenue models run after ingestion succeeds
  • output checks compare revenue against expected ranges
  • analytics is notified if the change looks unusual
  • failed runs stop downstream reports from using incomplete data

You can review the dbt command, dependencies, schedule, and checks before this goes live.

Mage helps teams run dbt as part of the larger system around it.

Ingest the data. Run the models. Check the output. Deliver the result. Explain what changed.

How Mage operates

Upstream Confirm source data arrived, schemas are expected, and inputs are fresh before models run.

Models Trigger dbt models inside workflows that understand the surrounding data process.

Checks Catch missing inputs, failed models, unusual output changes, and incomplete results.

Downstream Pause reports, alerts, exports, or agents when transformed data is not safe to use.

Explain Connect dbt failures to upstream sources, workflow steps, checks, and downstream impact.

Schedule Coordinate dbt with ingestion, delivery, reporting, and other production routines.

Review Review commands, dependencies, checks, schedules, and affected workflows.

Deliver Send modeled data to reports, applications, teams, and AI systems.

Before and after dbt matters

dbt transforms data. But production data work rarely starts or ends there.

Source data needs to arrive first. Inputs need to be fresh. Schemas need to be expected. Models need to run in the right order. Outputs need to be checked before reports, exports, applications, or agents depend on them.

Mage helps orchestrate the full path around dbt so transformations happen with the context they need.

Keep modeled data safe to use

A successful dbt run is not always the same as a trustworthy outcome.

Mage can check freshness, completeness, unexpected changes, downstream dependencies, and delivery timing around your dbt work. If something looks wrong, Mage can pause downstream steps, alert the right team, explain what happened, and help repair the workflow.

dbt stays focused, Mage runs the systems

Keep using dbt for the transformations your team trusts. Use Mage to connect those transformations to ingestion, schedules, checks, alerts, reports, applications, and AI systems.

The result is not just modeled data. It is modeled data that arrives on time, passes the right checks, and reaches the places that depend on it.

Put dbt inside the workflow that depends on it.

“Run these dbt models after the warehouse load finishes.”
“Stop the executive report if dbt revenue checks fail.”
“Tell me which upstream source caused this model to break.”
“Add a check for unusual changes in daily active users.”
“Notify analytics when a dbt run fails twice.”
“Show me which workflows depend on this model.”
“Run these dbt models after the warehouse load finishes.”
“Stop the executive report if dbt revenue checks fail.”
“Tell me which upstream source caused this model to break.”
“Add a check for unusual changes in daily active users.”
“Notify analytics when a dbt run fails twice.”
“Show me which workflows depend on this model.”
“Show me which workflows depend on this model.”
“Notify analytics when a dbt run fails twice.”
“Add a check for unusual changes in daily active users.”
“Tell me which upstream source caused this model to break.”
“Stop the executive report if dbt revenue checks fail.”
“Run these dbt models after the warehouse load finishes.”
“Show me which workflows depend on this model.”
“Notify analytics when a dbt run fails twice.”
“Add a check for unusual changes in daily active users.”
“Tell me which upstream source caused this model to break.”
“Stop the executive report if dbt revenue checks fail.”
“Run these dbt models after the warehouse load finishes.”