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Which Is Better, Azure Data Factory Or Azure Databricks?

 Which Is Better, Azure Data Factory Or Azure Databricks?

Data plays a crucial role in today’s technically advanced world. Data is stored in an unstructured format for various business uses, especially for analytics. Processing unstructured data is a complex process requiring a lot of time, resources, and effort. To eliminate hurdles involved in data processing, we need advanced technology solutions. Azure Data Factory and Databricks are cloud solutions that streamline the entire ETL process and provide a robust foundation for analytics. Let us understand these two solutions and make a comparison to find out which is better. 


Azure Data Factory vs. Databricks is the battle between the two widely used data integration tools. Both Azure Data Factory and Databricks are capable of handling structured and unstructured data. However, they come with their upsides and downsides. Azure Data Factory acts as an orchestration tool for data integration services. 

The primary role of Azure Data Factory is to carry out ETL workflows and orchestrate data transmission at scale. On the other hand, Azure Databricks acts as a single collaboration platform. The main aim of the tool is to help data engineers and data scientists to perform ETL and build ML models. In this head-to-head comparison guide, we will compare two powerful technologies of the cloud computing world.

What is Azure Data Factory? 

Azure Data Factory or ADF is a cloud-based PaaS (Platform as a Service) that the Microsoft Azure platform offers. The pre-built connectors make the tool suitable for hybrid Extract-Load-Transform (ELT), Extract-Transform-Load (ETL), and many other data integration pipelines. 

What are the requirements for Azure Data Factory?

Azure Data Factory requires the following essential components – 

  • Pipeline – A pipeline is the most important component. It is a logical group activity used to perform the unit of work. Single pipeline performs different actions like blob storage.
  • Activities – Activities represent the unit of work in the pipeline. It includes the activities used to copy the blob data for the storage table for transferring data into storage.
  • Datasets – Datasets represent the data structures within the data store. These point to the data activities which need to use in inputs and outputs.
  • Triggers – Triggers define the way to execute in the pipeline. These are determining when we are beginning our execution of the pipeline. It further contains three types which are schedule trigger, window trigger, event-based trigger.
  • Integration runtime – Integration runtime contains the computing infrastructure providing data integration capabilities, like data movement or data flow.

You can get this course Azure Data Factory Training available online to gain expertise in Business Process management to advance your career in the direction of Azure Data Factory.

What is the need for Azure Data Factory?

Large organizations have a lot of data from their customers stored in different databases that need to be transformed into standardized form and loaded into an Azure Sequel Database, a data warehouse that will allow you to see that data and make it consumable through complex analytics like business intelligence and machine learning, giving you insights into customer profiles and the ability to find customer issues that you can address. Thus, they require Azure Data Factory to consume the data, standardize it, and prepare it for analysis.

What are the benefits of Azure Data Factory?

Following are some key benefits of Azure Data Factory – 

  • Properly managed: As the deployment process of traditional ETL tools is complex, organizations need experts to install, configure, and maintain data integration environments. However, this is not the case with Azure Data Factory. Microsoft takes care of its proper management and utilizes Azure Integration Runtime to handle data movements. 


  • Minimal coding – Azure Data Factory enables developers to transform data by mapping data flows. Users can create code-free transformations to reduce the turnaround time for data analytics. Hence, it improves business productivity. 


  • Graphical user interface: Unlike traditional ETL platforms, Azure Data Factory provides a graphical user interface where drag-and-drop features quickly create a data integration pipeline. The best part about GUI is that such developments help users avoid configuration issues. 

What are the uses of Azure Data Factory?

Azure Data Factory is used to create and schedule data-driven workflows, or pipelines, and take data from a variety of data stores. It can connect to all necessary data and processing sources, including SaaS services, file sharing and other online resources. You can design data pipelines to move large amounts of data at specific intervals or all at once. 

Azure Data Factory is essentially utilized for serverless data migration and transformation activities such as Building code-free ETL/ELT processes in the cloud Building visual data transformation logics Staging data for transformation Running SSIS packages and moving them to the cloud executing a pipeline from Azure logic apps attaining continuous integration and delivery (CI/CD).

What is Azure Databricks? 

Azure Databricks is another popular ETL and data engineering tool. It is slightly different from Azure Databricks. Unlike Azure Data Factory, a PaaS tool, Azure Databricks is a SaaS-based data engineering tool. It helps you process and transforms massive data quantities to build ML models. Databricks supports various cloud services, including AWS, Azure, and GCP. 

What are the requirements for Azure Databricks?

  • Freedom – The developers in Azure Databricks have the freedom to alter the code activities by using various performance optimization techniques which enhance the capabilities of data processing. Azure Databricks support the spark clusters, it will handle more data efficiently, and the data factory connects to various data sources.
  • Seamless integration – By using Azure Databricks, you can seamlessly integrate open-source libraries and their access by the most recent versions. 
  • Global scalability – Due to the global scalability of Azure, we are easily creating the clusters and building the managed spark environment. Using Azure machine learning, data bricks are giving us access to automated machine learning capabilities that enable the algorithms.

What is the need for Azure Data Factory?

Unlike many enterprise data companies, Azure Databricks does not force you to migrate your data into proprietary storage systems to use the platform. Instead, you configure an Azure Databricks workspace by configuring secure integrations between the Azure Databricks platform and your cloud account, and then Azure Databricks deploys compute clusters using cloud resources in your account to process and store data in object storage and other integrated services you control.

Azure Databricks workspaces meet the security and networking requirements of some of the world’s largest and most security-minded companies. Azure Databricks makes it easy for new users to get started on the platform. It removes many of the burdens and concerns of working with cloud infrastructure, without limiting the customizations and control experienced data, operations, and security teams require.

What are the benefits of Azure Databricks?

Following are some key benefits of Azure Databricks – 

  • Integration – Databricks seamlessly integrates with Azure to drive big data solutions with ML tools in the cloud. Users can visualize the ML solutions in Power BI using the Databricks connector. 


  • Collaboration – Databricks instantly bring the scripts written in notebooks to the production phase. Multiple members can efficiently build data modelling and machine learning applications using the collaborative feature. 


  • Adaptability – Databricks allow different programming languages like SQL or Python to interact with Spark. The Spark-based analytics incorporates Language API at the backend to facilitate its interaction with Spark. That said, Databricks is regarded as highly adaptive. No matter which tool you choose, contacting the experts is important. 

What are the uses of Azure Databricks?

Azure Databricks is used to process, store, clean, share, analyze, model, and monetize their datasets with solutions from BI to machine learning. With the help of Azure Databricks platform, you can build and deploy data engineering workflows, machine learning models, analytics dashboards, and more.

The Azure Databricks workspace provides a unified interface and tools for most data tasks, including data processing workflows scheduling and management and working in SQL generating dashboards and visualizations. Databricks has a strong commitment to the open-source community. Databricks manages updates of open-source integrations in the Databricks Runtime releases. 

Key Differences Between Azure Data Factory Vs. Databricks 

Azure Data Factory and Azure Databricks use a similar architecture to help users perform scalable data transformation. According to reports global data creation will rise to more than 180 zettabytes by 2025. Anticipating the growth of data creating, organizations are adopting cloud computing solutions. Before you choose, it is important to learn their major differences. 


  • Ease Of Usage – With Azure Data Factory, users can quickly perform complex ETL processes. The drag-and-drop feature allows users to create and maintain data pipelines visually. On the contrary, Databricks uses multiple programming languages, including Python, Java, R, Spark, or SQL, during data engineering and data science project. So, here Azure Data Factory is easier to use than Data bricks. 


  • Purpose – Azure Data Factory is primarily used for ETL processes and orchestrating large-scale data movements. On the other hand, Databricks is like a collaborative platform for data scientists. They can perform ETL and build machine-learning models under a single platform. Both platforms are suitable for different purposes. Hence, the choice between the two tools depends on the user’s needs. 


  • Data Processing – Enterprises often perform stream or batch processing when working with large data volumes. While streaming data deals with archived or live data based on the application, batch processing deals with bulk data. Though both Azure Data Factory and Databricks can effectively support streaming and batch options, the former does not offer live streaming. So, if you are looking to use the live streaming feature, Databricks wins the case. However, if you want a fully managed data integration service that supports batch and streaming services, go ahead with Azure Data Factory. 

Some other differences between Azure Data Factory and Azure Databricks –

Azure Data Factory Azure Databricks
Azure Data Factory uses .Net, python, and PowerShell language in the Azure data factory. Azure Databricks uses Python, Scala, and R languages in Azure data bricks.
Azure Data Factory uses ETL or ELT for data movement in the Azure data factory. Azure Databricks uses collaboration and preparation of data in Azure data bricks.
Azure Data Factory contains the data integration tools of GUI. Azure Databricks does not contain data integration tools for GUI.
Azure Data Factory offers a layer of data integration and transformation. Azure Databricks does not offer any layer of data integration and transformation.
Azure Data Factory offers the options of drag and drop. Azure Databricks does not offer any option of drag and drop.
Azure Data Factory is the most important tool for loading data through ETL. Azure Databricks is extremely flexible and beginner-friendly. It makes distributed analytics much easier to use.

Similarities between Azure Data Factory and Azure Databricks

Some similarities between Azure Data Factory and Azure Databricks include – 

  • Both Azure Data Factory and Databricks contains structured and unstructured data.
  • Both Azure Data Factory and Azure Databricks contain data velocity in batch streaming and real-time.
  • Both Azure Data Factory and Databricks use a web browser tool for development respectively.
  • Both Azure Data Factory and Databricks follow a pay-as-you-go plan.


As the current digital revolution continues, using big data technologies will become a necessity for many organizations Thus, whether to use Azure Data Factory or Databricks can vary depending on the purpose, scope, timeframe, project size, organizational needs, and other factors. They are high quality and valuable cloud-based tools for organizations that want to migrate, aggregate, and transform data. With a solid understanding of and training in Azure Data Factory or Databricks, you will be able to evaluate and execute your organization’s need confidently.



1. What is Microsoft Azure?

Microsoft Azure is a cloud-computing platform. The service provider can set up a managed service in Azure to allow users to get access to the services on demand.

2. What is the use of Azure Data Factory?

Azure Data Factory is generally used for ETL processes, data movement, and data orchestration.

3. What is the advantage of using Azure Databricks?

Azure Databricks is used because it helps in real-time data collaboration and data streaming. 

4. Is Azure Databricks an ETL tool?

Yes. Databricks is an AI-based data ETL tool. It helps organizations accelerate the functionality and performance of ETL pipelines. 

5. What is an Azure Synapse?

Azure Synapse integrates analytical services for bringing enterprise data warehouse and big data analytics under a single platform.

6. What is the need for Azure Data Factory?

With an increasing amount of big data, there is a need for a service like Azure Data Factory that can orchestrate and operationalize processes to refine the enormous stores of raw business data into actionable business insights. 

7. Which Azure Data Factory version is best for creating data flows?

Azure Data Factory V2 version is best for creating data flows.

8. What distinguishes Azure Data Factory from traditional ETL tools?

Big data analytics, data integration, code-free transformation, and UI-driven dataflows for mapping are some features that distinguishes Azure Data Factory from traditional ETL tools.

9. How to troubleshoot issues related to Azure Databricks?

Troubleshooting with Azure Databricks you can be done through documentation which has solutions for several common issues.

10. Is Azure Data Factory scalable?

Yes, Azure Data Factory is a scalable platform. It is inbuilt with parallelism and time-slicing features, allowing users to migrate large amounts of data to the cloud in a few hours.

Author Bio:

I am Korra Shailaja, Working as a Digital Marketing professional & Content writer in MindMajix Online Training. I Have good experience in handling technical content writing and aspire to learn new things to grow professionally. I am an expert in delivering content on the market demanding technologies like Mulesoft Training, Dell Boomi Tutorial, Elasticsearch Course, Fortinet Course, PostgreSQL Training, Splunk, Success Factor, Denodo, etc.


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