Azure Data Factory vs. Azure Databricks

Azure Data Factory (ADF) and Azure Databricks are both powerful ETL/ELT tools, but they serve different purposes and are optimized for different workloads. Below is a detailed comparison along with key use cases for each.

Comparison: Azure Data Factory vs. Azure Databricks

Feature	Azure Data Factory (ADF)	Azure Databricks

Type

Data integration and orchestration service

Unified analytics platform with big data processing

Processing Model

ETL (Extract, Transform, Load)

ELT (Extract, Load, Transform)

Data Transformation

Basic transformations using Data Flows and Data Factory activities

Advanced transformations using Spark (Scala, Python, SQL)

Performance

Best for moving and transforming structured data in a low-code manner

Best for processing large-scale unstructured and structured data

Code Complexity

Low-code/no-code GUI-based

Code-based, requires Spark knowledge

Compute Engine

Uses Azure Integration Runtime

Uses Apache Spark clusters

Scalability

Scales well for small to medium workloads

Designed for large-scale data processing

Data Sources

Connects to 90+ data sources, including on-premises and cloud

Connects to cloud and on-prem data sources, optimized for big data

Cost

Pay-as-you-go, based on data movement and activity executions

Pay-as-you-go, based on Spark cluster usage

Use Case Best Fit

Best for ETL pipelines, data movement, and orchestration

Best for big data processing, machine learning, and real-time analytics

When should you use Azure Data Factory (ADF)?

ADF is best suited for data integration, ETL workflows, and orchestration when:

1. Extracting and Loading Data
   • Moving data from on-premises, cloud storage, or other services like SQL Server, Blob Storage, and Snowflake.
2. Orchestration of ETL Pipelines
   • Scheduling and managing workflows across multiple data sources.
3. Low-Code Transformations
   • Performing simple transformations using Mapping Data Flows.
4. Data Copying at Scale
   • Using Copy Activity for batch data transfer between multiple sources.
5. Hybrid Data Integration
   • Integrating on-prem and cloud data seamlessly with Self-hosted Integration Runtime.

📌 Example Use Cases:

• Loading raw data from SQL Server to Azure Blob Storage.
• Orchestrating a multi-step ETL pipeline to clean and enrich data.
• Moving data from on-prem databases to Azure Synapse for analysis.
• Running scheduled batch jobs for daily or hourly data refresh.

When to Use Azure Databricks?

Azure Databricks is ideal for data engineering, advanced analytics, and real-time data processing when:

1. Processing Large-Scale Data
   • Handling massive volumes of structured and unstructured data efficiently.
2. Real-Time and Streaming Data
   • Processing streaming data with Structured Streaming in Spark.
3. Complex Transformations & Machine Learning
   • Running machine learning models, data science workloads, and AI applications.
4. Big Data Analytics
   • Running distributed SQL, Python, or Scala workloads at scale.
5. Data Lake Processing
   • Managing and optimizing Delta Lake for large-scale data lakes.

📌 Example Use Cases:

• Transforming TBs of log files for fraud detection in real-time.
• Running AI/ML models on customer behavior data for predictions.
• Processing IoT sensor data for anomaly detection.
• Enriching data in a Delta Lake before moving it to Power BI for visualization.

Using Both Together (ADF + Databricks)

For end-to-end data workflows, both ADF and Databricks are often used together:

1. ADF handles data ingestion & orchestration (Extract & Load).
2. Databricks perform complex transformations & analytics (Transform).
3. ADF schedules and monitors jobs in Databricks to automate workflows.

📌 Example Architecture:

• Step 1: ADF moves raw data from on-prem to Azure Data Lake.
• Step 2: Databricks cleans, enriches, and transforms the data.
• Step 3: ADF copies the processed data to Azure Synapse or Power BI.

Final Takeaway

• Use ADF when you need a low-code, orchestration-focused, and simple ETL tool.
• Use Databricks when you need high-performance, big data processing and complex transformations.
• Use both together for scalable and efficient data pipelines.