SaaS dbt warehouse transformation pipeline
This data pipeline flowchart maps an hourly SaaS analytics transformation. CRM, billing, and support extracts first land in a raw warehouse schema. Before dbt builds reporting models, the pipeline checks whether every source arrived within its freshness service level. A late source marks downstream data stale and stops publication.
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Scenario
A SaaS company combines CRM, billing, and support data for recurring-revenue reporting. The diagram prevents stale or duplicate source data from reaching executive dashboards.
Key decisions
- Freshness SLA: Late source extracts halt publication rather than showing misleading metrics.
- dbt staging: Source-specific fields are standardized before joining business entities.
- Key test: Duplicate primary keys fail the transformation run.
- Semantic publication: MRR and churn definitions are published with their dbt documentation.
When to reuse this
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.
Frequently asked questions
Why test freshness before dbt models?
Why create staging models?
What does a primary-key test prevent?
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