Azure Data Factory or Databricks

Azure Data Factory or Databricks: which should you pick?


Azure Data Factory is your orchestration engine: think of it as a low-code / GUI tool to move and schedule data.

Connects to 100+ sources
Great for batch ETL, data movement
Best when you mainly need orchestration with minimal coding

Databricks, on the other hand, is your heavy-duty transformation engine powered by Spark.

Ideal for large-scale, distributed processing
Supports Python, Scala, SQL, ML workflows
Use it for really complex transformations, feature engineering, or real-time streams

So, when to use which?

If your job is mainly orchestration + data movement → Start with ADF
If your job involves big transformations, analytics, ML → Go with Databricks
And in most real-world scenarios use both together: ADF orchestrates, Databricks transforms.”

Here’s how they often pair up:

ADF ingests sales, CRM, ERP data into a data lake
Databricks cleans, enriches, trains models
So bottom line:

ADF = orchestration & movement
Databricks = transformation & ML