Better Data Engineering Part 1: Why dbt Matters

Why?

Why dbt?
Why another tool in the Modern Data Stack?
Why should a Data Engineer care?

Introduction

If you've been building pipelines long enough, you've probably dealt with:

• SQL scripts scattered across repos
• undocumented transformations
• inconsistent naming
• fragile pipelines that break silently
• analysts rewriting the same logic in dashboards

dbt solves these problems by bringing software engineering principles to SQL.

This blog series will walk you through the fundamentals of dbt and how to use it effectively as a Data Engineer.

Rule 1: Treat SQL Like Code

dbt forces you to think in terms of:

• modularity
• version control
• testing
• documentation
• CI/CD

SQL stops being “queries in a warehouse” and becomes a real codebase.

Rule 2: Transformations Belong in the Warehouse

Stop moving data around unnecessarily.

dbt embraces the idea:

“ELT over ETL — transform where the data lives.”

This reduces complexity and improves performance.

Rule 3: Build Trust with Tests

A pipeline without tests is a pipeline waiting to fail.

dbt makes testing:

• simple
• declarative
• automated

Start with:

• unique
• not_null
• relationships
• accepted_values

Rule 4: Document as You Build

Documentation is not an afterthought.

dbt generates docs automatically from your models, tests, and descriptions.

Your warehouse becomes self-explanatory.

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Stay tuned for Part 2!