dbt

Why dbt Changed Analytics Engineering

dbt brought software engineering to data transformation. Write SQL, get version control, testing, and documentation. Data pipelines that are actually maintainable.

My Experience

Every data warehouse project I work on uses dbt. The testing framework alone saves hours of debugging. The documentation generation ensures everyone understands the data models.

What Makes dbt Essential

1. SQL-Based Transformations - Write transformations in SQL, the language data people already know. No Python required, no new syntax. Focus on the logic, not the tooling.

2. Version Control - Your data transformations in Git. Code review for data logic. Rollback when things break. Treat data like software.

3. Testing Framework - Test your data assumptions. Unique keys, not-null values, referential integrity. Catch data quality issues before they become business problems.

4. Auto Documentation - Generate documentation from your models. Lineage graphs show data flow. Everyone understands where data comes from and what it means.

Where dbt Falls Short

Learning curve for the mental model—sources, models, tests. dbt Cloud pricing adds up for larger teams. Debugging compiled SQL can be tricky.

Who Should Use dbt

• Analytics engineers transforming data
• Data teams wanting engineering practices
• Organizations with data warehouses
• Teams needing data testing

dbt Core vs dbt Cloud

Factor	dbt Core	dbt Cloud
Price	Free	Paid
Hosting	Self-managed	Managed
Scheduling	External	Built-in
IDE	Local	Browser
Best For	Full control	Convenience

The Bottom Line

dbt is essential for modern data teams. The core is free and powerful. dbt Cloud adds convenience but isn’t required. Either way, dbt practices should be in your data stack.