Azure Data Factory Vs Databricks

Introduction to Data Factory Vs Data Bricks

Data is playing a very essential role in today's technology-driven world. Often data comes in an unstructured format and is stored for various business uses especially for analytics. Processing unstructured and semi-structured data is a complex process and requires a huge time, resources, and effort. To eliminate the hurdles involved in data processing we need advanced technology solutions. 

Azure Data Factory and Databricks are two cloud solutions that streamline the end-to-end process of ETL & integration and provide a strong foundation for analytics. ADF offers ETL & integration services whereas Databricks streamlines data architecture and provides a centralized platform for AI, data science, analytics, etc.

In this blog, we are going to understand ADF and Data factory and make a comparison between Azure data factory vs Data bricks. Also, we will discuss some of the key benefits of ADF and the benefits of the Data factory.

What is Azure Data Factory

Azure Data Factory is a serverless cloud ETL and data integration service offered by Azure. It is a hybrid cloud platform designed to support ETL and ELT operations. Unlike traditional ETL solutions, ADF comes with an advanced code-free UI that supports users in performing any complex operations. Also, it helps users with operations like defining datasets, building pipelines, and mapping data to destinations.

Azure Data factory performs all the ETL and integrations using components like datasets, pipelines, activities, triggers, integration runtime (IR), etc.

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Related Article: Azure Data Factory Interview Questions

What is Azure Databricks

Azure Databricks is a collaborative, spark-based, analytics platform that leverages Azure lakehouse to unify all your AI and Analytical workloads. It is highly flexible and supports major cloud providers like AWS, GCP, and Azure. Databricks offers different environments for Data Science, SQL, Machine Learning, Data Engineering, etc. Data engineering teams and Machine learning experts can work in a collaborative environment to work on data science projects.

Related Article: Databricks Tutorial

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Data Factory vs Databricks

The following table is designed with the intention to take different parameters into consideration and explains to you the major difference between Azure data factory and Azure databricks.

Parameter Data Factory DataBricks 
Purpose The major purpose of ADF is to build integrations for ETL and ELT operations Databricks is used for data preparation and collaboration.
Ease of Usage ADF offers easy-to-use drag-and-drop features to build & manage pipelines. Databricks uses the Notebook option to support different languages such as R, Python, Scala, java, etc. 
Flexibility in Coding In ADF developers can not alter or make changes to the backend code. Databricks is flexible & developers can alter and optimize code for better performance.
Supported Data Structures Both structured and unstructured data support. Both structured and unstructured data support.
Data Processing ADF supports only batch and steam processing but not live streaming Data processing like batch, stream, and live streaming is supported. 
Supported Languages Python, Powershell, .Net SQL, Python, R, Scala
Pricing Model Pay-as-you-go model Pay-as-you-go model


Benefits of Azure Data Factory:

Following are some of the notable advantages of using ADF:

  • No-code data workflows
  • Easy SSIS migration
  • 100+ data connectors
  • Easy to track every aspect
  • Cost-effective pricing model
  • Benefits of Azure Databricks
  • Supports coding in familiar languages
  • Team collaboration
  • Seamless integration with whole azure stack
  • Easy connectivity to a wide range of data sources
  • Supports small jobs too.
  • Clear documentation & support

Wrapping up:

In this blog, we have discussed in detail data processing, and the role of Azure data factory and Databricks. I believe this blog post has helped you in clearing your confusion between ADF and databricks and given you a fair understanding of how these two technologies are different.

By Tech Solidity

Last updated on March 20, 2023