Why Use dbt When Databricks Can Handle Complex Transformations?

If you have already been using databricks for a long time, you know how powerful the tool is.

Spark + SQL Python + Delta Lake = one of the most flexible data platforms in the data engineering world.

So, why then dbt? “Why introduce another tool like dbt into the mix? Isn’t databricks enough for building and managing data transformation?”

It’s a fair question- and the answer is simple and strategic. Because how you write, test, deploy, and collaborate on your transformations matters just as much as what you transform. Think of dbt as bringing software engineering discipline into the analytics engineering world.

You still need Databricks or Snowflake — but with dbt, you get:

✔cleaner pipelines
✔better collaboration
✔greater confidence in your data

Databricks is great for running transformations.

DBT is great for organising, testing, documenting, and deploying them.dbt is not about replacing your compute engine (like Databricks or Snowflake) — it’s about bringing software engineering best practices to your data transformations.

Let’s understand this in detail:

1. Modularity:

• Databricks notebooks are…