Databricks vs Azure Data Factory: When to Use Which in ETL Pipelines
Introduction: Two Powerful Tools, One Common Question
If you work in data engineering, you’ve probably faced this question:
Should I use Azure Data Factory or Databricks for my ETL pipeline?
Both tools can move and transform data, but they serve very different purposes.
Understanding where each tool fits can help you design cleaner, faster, and more cost-effective data pipelines.
Let’s explore how these two Azure services complement each other rather than compete.
What Is Azure Data Factory (ADF)
Azure Data Factory is a data orchestration service.
It’s designed to move, schedule, and automate data workflows between systems.
Think of ADF as the “conductor of your data orchestra” — it doesn’t play the instruments itself, but it ensures everything runs in sync.
Key Capabilities of ADF:
- a. Connects to 100+ data sources using built-in connectors.
- b. Performs lightweight transformations using Data Flows.
- c. Orchestrates external compute systems like Databricks, Synapse, or Functions.
- d. Triggers pipelines on schedule or event.
Best For:
- a. Moving data from multiple sources (SQL, API, Blob, SAP, etc.)
- b. Scheduling and monitoring ETL jobs
- c. Low-code data integration without heavy coding
What Is Azure Databricks
Azure Databricks is a data processing and analytics platform built on Apache Spark.
It’s designed for complex transformations, data modeling, and machine learning on large-scale data.
Think of Databricks as the “engine” that processes and transforms the data your ADF pipelines deliver.
Key Capabilities of Databricks:
- a. Handles massive data transformations at scale using Spark.
- b. Supports multiple languages (Python, SQL, R, Scala).
- c. Uses Delta Lake for ACID transactions and schema enforcement.
- d. Ideal for building machine learning pipelines and data lakes.
Best For:
- a. Advanced transformations and aggregations.
- b. Real-time streaming and data science workloads.
- c. Data preparation for analytics and AI.
ADF vs Databricks: A Detailed Comparison
| Feature | Azure Data Factory (ADF) | Azure Databricks |
|---|---|---|
| Primary Purpose | Orchestration and data movement | Data processing and advanced transformations |
| Core Engine | Integration Runtime | Apache Spark |
| Interface Type | Low-code (GUI-based) | Code-based (Python, SQL, Scala) |
| Performance | Limited by Data Flow engine | Distributed and scalable Spark clusters |
| Transformations | Basic mapping and joins | Complex joins, ML models, and aggregations |
| Data Handling | Batch-based | Batch and streaming |
| Cost Model | Pay per pipeline run and Data Flow activity | Pay per cluster usage (compute time) |
| Versioning and Debugging | Visual monitoring and alerts | Notebook history and logging |
| Integration | Best for orchestrating multiple systems | Best for building scalable ETL within pipelines |
In simple terms, ADF moves the data, while Databricks transforms it deeply.
When to Use ADF
Use Azure Data Factory when:
- You need to integrate multiple systems quickly using connectors.
- Your transformations are simple (rename columns, filter, map).
- You want a visual pipeline with minimal coding.
- You need scheduled data movement between storage or databases.
- Your organization prefers low-code or no-code tools.
Example:
Copying data daily from Salesforce and SQL Server into Azure Data Lake.
When to Use Databricks
Use Databricks when:
- Your ETL process involves complex business rules or logic.
- You are handling very large datasets.
- You need real-time streaming or event-based transformations.
- You plan to build a Lakehouse with Delta Lake.
- You want to combine data engineering with data science and AI.
Example:
Transforming millions of sales records into curated Delta tables with customer segmentation logic.
When to Use Both Together
In most enterprise data platforms, ADF and Databricks work together.
Typical Flow:
- ADF orchestrates the pipeline schedule.
- ADF calls a Databricks Notebook using a Databricks activity.
- Databricks performs heavy data transformations and writes the output to Delta Lake.
- ADF then moves the transformed data to Azure Synapse or Power BI for reporting.
This hybrid approach combines the automation of ADF with the computing power of Databricks.
Example Architecture:
ADF → Databricks → Delta Lake → Synapse → Power BI
This is a standard enterprise pattern for modern data engineering.
Cost Considerations
- a. ADF: Cost is based on pipeline runs, data movement, and data flow compute time. Ideal for lighter workloads and orchestration.
- b. Databricks: Cost depends on cluster runtime and size. Ideal for large-scale transformation and compute-heavy operations.
Using ADF for orchestration and Databricks for processing ensures you only pay for what you need.
Best Practices
- Use ADF for scheduling, monitoring, and orchestration.
- Use Databricks for transformations, modeling, and advanced analytics.
- Always use Auto-Termination on Databricks clusters to save cost.
- Maintain parameterized and modular pipelines in ADF.
- Integrate both tools using Service Principals and Key Vault for secure authentication.
Azure Data Factory and Azure Databricks are not competitors.
They are complementary tools that together form a complete ETL solution.
- a. Use ADF to orchestrate and move data.
- b. Use Databricks to transform and enrich it.
Understanding their strengths helps you design data pipelines that are reliable, scalable, and cost-efficient.
We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com
