Phase 2: Enterprise Data Engineering · 55 min · Python · dbt · Snowflake

dbt Data Quality & Medallion Architecture

Bronze in, Silver cleaned, Gold served. Every layer tested.

What you will learn

Structure a dbt project: models/, tests/, macros/, snapshots/, sources.yml
Implement Medallion Architecture: Bronze (raw), Silver (cleaned/typed), Gold (business-ready marts)
Write dbt tests: unique, not_null, relationships, accepted_values, custom singular tests
Configure incremental models with merge strategy and _etl_loaded_at partitioning
Use dbt snapshots for SCD2: tracking slowly-changing dimensions

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Structure a dbt project: models/, tests/, macros/, snapshots/, sources.yml
Implement Medallion Architecture: Bronze (raw), Silver (cleaned/typed), Gold (business-ready marts)
Write dbt tests: unique, not_null, relationships, accepted_values, custom singular tests
Configure incremental models with merge strategy and _etl_loaded_at partitioning
Use dbt snapshots for SCD2: tracking slowly-changing dimensions

The Problem

Data quality is the silent killer of enterprise AI. If your source data has nulls where there shouldn't be nulls, duplicates that create phantom records, or type mismatches that break your parser, your RAG system will return garbage. dbt gives you a framework for testing data quality at every layer using the medallion architecture: bronze (raw), silver (cleaned), gold (served).

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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