Azure Data Factory vs. Databricks: When to Use What?

In today’s cloud-first world, enterprises have no shortage of data services. But when it comes to building scalable, reliable data pipelines, two names often dominate the conversation: Azure Data Factory (ADF) and Azure Databricks.

Both are powerful, both live within the Azure ecosystem, and both can move and transform your data. But they’re not interchangeable, each has a sweet spot. Let’s break it down.

Azure Data Factory: The Orchestrator

Think of ADF as the ETL/ELT workhorse of Azure.

• Purpose: Low-code/no-code orchestration of data pipelines.
• Strengths:
  • Connects to 100+ data sources out-of-the-box
  • Great for batch ETL, scheduled workflows, and data movement
  • Easy to monitor and manage via a GUI
  • Strong integration with Synapse Analytics and Azure Data Lake
• Best For:
  • Moving data between systems (on-prem → cloud, SaaS → lakehouse)
  • Classic ETL workloads
  • Teams who want a visual designer without heavy coding

Use ADF when the job is about orchestration, scheduling, and integrating data from multiple sources.

Azure Databricks: The Engine

Databricks is where data engineering and advanced analytics come alive.

• Purpose: A scalable Spark-based data platform for transformation, analytics, and machine learning.
• Strengths:
  • Handles massive-scale distributed data processing
  • Great for unstructured, semi-structured, and real-time data
  • Supports Python, Scala, R, SQL, and notebooks for flexibility
  • Built for advanced transformations, machine learning, and AI
• Best For:
  • Complex data wrangling and big data transformations
  • Real-time streaming data
  • Feature engineering and ML model training
  • Teams with strong coding and data science expertise

Use Databricks when the job is about heavy-duty transformation, big data, or advanced machine learning.

The Combined Approach

The truth is, most modern data platforms use both:

• ADF orchestrates the workflow → pulling data from multiple sources, scheduling jobs, and handling dependencies.
• Databricks transforms and analyzes → cleaning, enriching, modeling, and scaling workloads.

For example:

1. ADF ingests raw sales, CRM, and ERP data into Azure Data Lake.
2. Databricks cleans, joins, and enriches the data, then applies ML for demand forecasting.
3. ADF orchestrates the final load into Power BI datasets for reporting.

Together, they give you a low-code orchestrator + a high-powered transformation engine.

Quick Decision Guide

• Need a simple pipeline? Start with ADF.
• Need scalable transformations or ML? Go with Databricks.
• Need both orchestration + transformation? Use ADF to call Databricks notebooks — best of both worlds.

Takeaway:

• Azure Data Factory = Orchestration & Data Movement
• Azure Databricks = Transformation & Advanced Analytics
• Together, they form a modern, flexible Azure data stack.