Favorita: dbt + Prefect Orchestration
Wrap the Favorita dbt project in a Prefect flow so your transformations run on a schedule, retry on failure, and tell you when something breaks — instead of someone running dbt by hand and hoping.
Primary Outcome
A scheduled, observable dbt pipeline that runs itself and alerts you when a transformation fails.
Solution
A Prefect flow that schedules the dbt build, retries transient failures, and alerts the team the moment a transformation or test breaks.
Deliverables
A Prefect flow that runs the dbt build end to end
Task-level retries, logging, and failure notifications
A deployment with a schedule (daily refresh out of the box)
Parameterized runs so you can target specific models or environments
Strategic Context
Orchestration is where analytics becomes infrastructure. A model that only runs when someone remembers to run it isn't a system — it's a chore. Scheduling plus alerting is the cheapest reliability upgrade most data teams can make.
Technical Architecture
Orchestration is where analytics becomes infrastructure. A model that only runs when someone remembers to run it isn't a system — it's a chore. Scheduling plus alerting is the cheapest reliability upgrade most data teams can make.
Problem Statement
dbt models that depend on a human remembering to run them lead to stale data, silent test failures, and dashboards nobody trusts after the first missed refresh.
Links
What's Included
A Prefect flow definition that orchestrates the dbt project, deployment configuration with scheduling, retry and logging setup, and notification hooks for failures.
FAQs
Why Prefect instead of Airflow?
Prefect is lighter to stand up and friendlier for Python-first teams. The pattern ports to Airflow or Dagster if you prefer.
Does it need the dbt + BigQuery template?
It's built to wrap that project, but it works with any dbt project — point the flow at your own.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4
Primary Outcome
A scheduled, observable dbt pipeline that runs itself and alerts you when a transformation fails.
Problem Statement
dbt models that depend on a human remembering to run them lead to stale data, silent test failures, and dashboards nobody trusts after the first missed refresh.
Solution
A Prefect flow that schedules the dbt build, retries transient failures, and alerts the team the moment a transformation or test breaks.
Links
Deliverables
A Prefect flow that runs the dbt build end to end
Task-level retries, logging, and failure notifications
A deployment with a schedule (daily refresh out of the box)
Parameterized runs so you can target specific models or environments
What's Included
A Prefect flow definition that orchestrates the dbt project, deployment configuration with scheduling, retry and logging setup, and notification hooks for failures.
Strategic Context
Orchestration is where analytics becomes infrastructure. A model that only runs when someone remembers to run it isn't a system — it's a chore. Scheduling plus alerting is the cheapest reliability upgrade most data teams can make.
FAQs
Why Prefect instead of Airflow?
Prefect is lighter to stand up and friendlier for Python-first teams. The pattern ports to Airflow or Dagster if you prefer.
Does it need the dbt + BigQuery template?
It's built to wrap that project, but it works with any dbt project — point the flow at your own.
Technical Architecture
Orchestration is where analytics becomes infrastructure. A model that only runs when someone remembers to run it isn't a system — it's a chore. Scheduling plus alerting is the cheapest reliability upgrade most data teams can make.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4