“Create a data pipeline flowchart for hourly SaaS CRM, billing, and support extracts entering a warehouse raw schema, with freshness checks, dbt staging and primary-key tests, customer and subscription models, MRR and churn marts, and refreshed BI dashboards.”
Read the decisions behind it.
Late source extracts halt publication rather than showing misleading metrics.
Source-specific fields are standardized before joining business entities.
Duplicate primary keys fail the transformation run.
MRR and churn definitions are published with their dbt documentation.
Use this template for SaaS warehouse transformations where customer, subscription, and support facts must agree. It fits teams using dbt to govern reusable business metrics.
Other examples in this family.
Common questions
Why test freshness before dbt models?
A complete-looking model can still be wrong if one operational source arrived late.
Why create staging models?
They standardize source fields and isolate source-specific cleanup from business logic.
What does a primary-key test prevent?
It catches duplicate entities that could inflate MRR, customer, or churn calculations.