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dbt → warehouse → tests → docs

Transform raw landed data into a tested, documented star schema with dbt, and gate every change on data-aware CI.

At a glance

Where dbt · tech-dbt · tech-postgres · tech-duckdb

Learning objectives

  • Layer staging/intermediate/marts
  • Write tests that protect the grain
  • Run data-aware CI against a staging schema

Security scan

Last sweep 2026-10-24: 0 finding(s), 0 marked as examples.

Implementation

  • [object Object]
  • [object Object]
  • [object Object]
  • Incremental on fct_orders with a 3-day lookback.

Tests

  • unique + not_null on the fct grain key
  • relationships from fct to dims
  • accepted_values on status

Failure Scenarios

  • Late-arriving order missed → lookback window too short
  • Grain key not unique → a join fanned out upstream
  • CI green but prod differs → CI ran on a different target/schema

Production Hardening

  • CI builds to a per-PR staging schema and runs full tests
  • Source freshness checks on raw
  • Exposures declared for dashboards

Registry stats

Verification community · Sep 27, 2026
Completeness 81
Trust multiplier ×0.25
Composite 32.65 · adequate
Freshness 27 days since last source check
Difficulty beginner
Version 1.3.0
License MIT
Security flags 4

Repository Structure

dbt-warehouse-tests/
  models/staging/
  models/intermediate/
  models/marts/
  models/schema.yml     # tests + descriptions
  tests/                # singular tests
  .github/workflows/ci.yml

Architecture

raw.orders ─▶ stg_orders ─▶ int_orders_enriched ─┐ raw.customers ─▶ stg_customers ──────────────────┼─▶ fct_orders ─▶ marts seeds/ ─▶ dim_date ──────────────────────────────┘ (tests + docs on every node; CI on staging schema)

Setup

  • dbt deps && dbt seed
  • dbt build --target dev
  • dbt docs generate && dbt docs serve

Sample Data

dbt seeds — customers.csv, orders.csv (small, deterministic)