▷ Azure Data Factory Tutorial for Beginners (2023) (2023)

Azure Data Factory is a cloud-based platform. The platform or rather an ecosystem allows you to develop, build, deploy and manage the application on the cloud storage. The cool thing about the platform is that you can do everything in the cloud. However, the memory of your physical device will be saved. And everything you do is stored in the cloud.

Seeing the Azure platform from the perspective of the future has enormous scope. When Microsoft comes up with a concept, it's sure to be futuristic. Therefore, learning it now is an excellent step towards your career.

You need to know how practical Azure Data Factory works before using it. Let's consider a scenario where you have a lot of data and you can't sort it. Azure Data Factory helps you analyze your data and also transfer it to the cloud.

To introduce the Azure Data Factory to you, we can say that the Azure Data Factory can store data, analyze it in a suitable way, help you to transfer your data through pipelines, and finally you can publish your data. You can also use some third-party apps like R and Spark to visualize your data.

If you want to enrich your career with aMicrosoft Azure certified expert, then login: "Azure online training". This course will help you excel in this field.

TheAzure-Tutorialis basically designed for developers who have Azure subscriptions. Azure implementation will fall into website, application and software development. Therefore, Azure will be more beneficial for those who want to undertake such projects.

Azure Data Factory tutorial for beginners

If you want to use Azure Data Factory, you must have knowledge of Azure Data Factory tutorials. Let us know what exactly the Azure Data Factory tutorial is and how useful it is. With the help of the Azure Data Factory tutorial, you will know howAzure data factoryactually works. You will also be made aware of the importance of the Azure Data Lake.

The Azure Data Factory tutorial walks you through copying your data from Azure SQL to Azure Data Lake. After that, you can also visualize your data using third-party sources like Power BI.

You will also learn more about analytics. Analytics can also be operated by using U SQL to process the data.

Nowadays we get a large amount of data from many resources. The result of this increasing amount of data is that it becomes very difficult for us to manage, store and analyze the data at the same time. For this reason, we need to use Azure Data Factory, which will help you store, analyze and transfer large amounts of data.

There are various steps and terms associated with Azure Data Factory, such as: B. Pipelines, Azure Data Lake, storage. Let's get acquainted with all these terms and get detailed information about them. As mentioned before, Azure Data Factory collects this data when you have stored data, transfers this data through pipelines, and finally you can use this data to publish or visualize using various sources.

Here is a step-by-step illustration.

  • Gather the data and connect them:Data in the pipeline can be copied or pushed to the cloud source data stores, or the same data can be moved locally as well.
  • Transforming the data:Computer services can help you transform or process the data that is already centralized in the cloud. Computer services are Hadoop, Spark, R, etc.
  • Publication of the data:Already structured, analyzed and refined data is now collected in the Azure Data Factory Warehouse. It is monitored and published in the Azure Data Warehouse.
  • Monitoring of your data:PowerShell, Azure Monitor is available in the Azure portal to help you with pipeline monitoring. Azure Data Factory works on a data-driven workflow with a structure so the data can be easily moved and transferred. Azure Data Factory doesn't work with a single process. It has various small components that work independently and work successfully in combination.
  • Pipeline:A unit of work performed by logical grouping activities is called a pipeline. Pipelines can be single or multiple. Various tasks are performed by the pipeline at the same time, such as: B. Transform, analyze and store
  • Activity:Processing steps of the pipelines are represented with the help of activity. For example, copy activity is often used to move data from one source to another.
  • Records:Data sources that exist in the data stores are represented by the record structures. We may also classify this data in our activities.
  • Linked Services:It can be defined as a bridge used to connect Azure Data Factory to external resources. Computer resources and data storage are types of linked services.
  • Triggers:Triggers, as the name suggests, trigger something. When pipeline execution needs to be disabled, the processing unit determines this with a trigger. This unit is called a trigger. Another feature of the trigger is that we can schedule this process in advance, so we can trigger and disable the process at a specific point.
  • Control flow:It is an extension of the activities carried out via pipeline. We can also say that it is like a thread or chain that arranges activities in an order.

▷ Azure Data Factory Tutorial for Beginners (2023) (1)

Components of SQL Data Warehouse

  • Data Warehouse Units:Data warehouse units (DWUs) are the measure of data or resources received from SQL. This storage is allocated to the data warehouse.
  • Scanning/Aggregation:Scan/Aggregation scans the generated query. This work is I/O and CPU based.
  • Burden:Load is the measure of how much data the data warehouse could actually upload.
  • Create table as selection (CTAS):CTAS allows users to make a copy of the spreadsheet. It reads, edits, distributes and writes the table.

Structure of the Azure Data Factory

Let's dig deeper into the structure of Azure Data Factory. Let's say you have some data, it can be in the form of mobile data or some other kind. Now this data can be transferred to your Output Cloud via a pipeline. Pipeline is SQL or Hype that performs various operations on data and transfers them to the output cloud. We'll learn more about pipelines in a few more points. Output data is the structural form of your data as it is already transformed and analyzed in the pipeline. This data is finally stored in the sea data.

  • Organisation:Azure DevOps Organization works very much like any physical organization out there. It is a group or division of the same projects combined together.
  • Projects:Projects in DevOps are agile and continuous testing, integration and deployment of the same project is ongoing.

Create Azure Data Factory with theAzure-Portal

Step 1:Search for "Create a resource" and search for "Data Factory". Click the Create icon.

(Video) Azure Data Factory | Azure Data Factory Tutorial For Beginners | Introduction to Azure Data Factory

Step 2:Give your data factory a name. Select your resource group. Give it a path and select the version you want.

Step 3:Click Create.

Your data factory is thus ready to be filled with further data.

Azure SQL Stretch Database andSQL-DataWarehouse

SQL stretch database:

The SQL database acts as an interface between the user's raw data and Azure's data lake. The SQL database is processed and transformed to the cloud. The data is processed for further classification. Hot data is the one that users access frequently. And cold data is data that is not accessed often.

To restore or retrieve the data when needed, we simply have to enter a query. And the data, wherever it has been categorized, is searched.

Benefits of SQL stretch database

The on-premise storages are expensive and require additional effort to inject the database queries. SQL is comparatively simpler and cheaper than most other storage platforms. SQL extracts the data in the cloud. Therefore, it is faster and its maintenance is also minimal.

As the data is transformed, SQL keeps it extremely safe and secure. Its encryption protects the confidentiality of data even while data transformation is in progress. Advanced security of the SQL keeps the stretch database absolutely safe and secure.

SQL Database Warehouse is a completely cloud-based platform and therefore extracts the data with ease. It uses a parallel architecture to retrieve the data type it is looking for. With the parallel architecture, the data is queried with the control mode and forwarded to the computer mode. Both the modes and the processes in between work in parallel.


Azure-DevOpshelps coordinate with the support team to make Azure application development and changes. However, with Azure DevOps, you can make minor changes to code and infrastructure without developer intervention.

Azure DevOps Services

Azure DevOps not only allows you to make minor changes, but it also helps the user to deploy and build the applications. His ministries are widely scattered. From creation to development to deployment, all functions run synchronously on the single cloud platform.


Azure Repository is very similar to Git repository. All codes are stored in the Azure repository to build an application. The tool is used by developers for coding and building. Specific developers are granted access to a specific Git portion of code.


The Azure pipeline acts as a pipeline to the target path. Wherever the code should combine or work at any given point, this pipeline injects the code there.

(Video) Azure Data Factory: Beginner to Pro [Live Event]


Board is something where you can create activities, track activities and distribute the development task to the team.

experimental plan:

A test plan is browser-based testing that is performed in an automated manner instead of manual testing.


Azure Data Factory requires NuGet package, npm, Maven package and many other such packages. Artifacts are compatible with all packages and are therefore very useful.

Collaboration tools:

These are the tools for team collaboration. The team is free to have their own customized dashboard and required widgets on their board.

Also read:Overview of Azure Arc

Azure DevOps-Portal

You perform all development tasks in the Azure DevOps portal. To create an Azure portal, you must first register with Azure services.

Step 1: Visit https://azure.microsoft.com/en-in/services/devops/ and click Start for free if you are not already registered for Azure services.

▷ Azure Data Factory Tutorial for Beginners (2023) (2)

Step 2:Once you've started, you'll be prompted to fill in the details.

▷ Azure Data Factory Tutorial for Beginners (2023) (3)

Step 3:Now you are in the Azure DevOps portal and need to create a new project and organization for yourself.

Step 4:Create new organization.

Step 5:Give your organization a name and select the path for your organization.

(Video) Azure Data Factory Tutorial | Introduction to ETL in Azure

Step 6:The organization is now created and you will be prompted to create your new project

Step 7:Give your project a name. And in the advanced settings select the version you will work on.

Step 8:Once you click create project, your project will be created successfully.

You can invite your teammates to the project.

Publish the ARM deployment project to DevOps

Step 1:Open Visual Studio and look for a new project tab.

Step 2:In the project you will find the Azure resource group.

Step 3:Use this to configure the new project and click Next.

Step 4:In Visual Studio, click Web Application in the Azure template.

Step 5:You can find the website.json file on the left side of File Explorer.

▷ Azure Data Factory Tutorial for Beginners (2023) (4)

Step 6:Publish this code by clicking Add code.

Step 7:You will get a new Git repository.

Step 8:Click Team Explorer and then right-click to select Sync.

Step 9:Click the Publish Git Repo button. And your project in the Azure DevOps organization will be published.

Step 10:Select the project and repository path where you want to publish this code, then click Publish Repository.

Azure Factory Data Lake-Dateisystem.

Microsoft Azure Data Lake: The manipulation of data can be understood in three basic steps. The first step is to provide you with output data which can be in mobile data form or other type, then it is transferred to the Azure factory data, and in the third step you can use your data with any 3rd party apps like R, Apache, spark etc.

There are two other important components that you should know. The first concept is storage, storage of data can be in gigabytes, terabytes, etc. This data is broad and big information. You can analyze this data as structured or unstructured data. Structured data contains specific information and unstructured data is lobby data.

(Video) Azure Full Course - Learn Microsoft Azure in 8 Hours | Azure Tutorial For Beginners | Edureka

The second concept is analysis, now analysis is also divided into two types. First kind of

Analysis is a monitor type in which you can generate your data. For example, building data, location, construction costs, area of ​​the building, lifespan of the building, amount of live load and dead load it supports. All of this building data is available to you in a structured manner.

Another type of analysis is using the Azure Data Factory in a map. If you have a debit card, you can learn about its transactions, the location of the card, its expiration date, and more. These are the two main concepts you need to know about Azure Data Factory.

bottom line

That was the insight into Microsoft Azure. The tutorial was intended for beginners who have not yet accessed the Azure Data Factory platform. If you're excited about Azure Data Factory from a professional perspective, it's the best skill to learn today that will help you make money tomorrow. Basically, all social media data is processed and optimized with a data factory.

Because Azure is a cloud-based platform, the load on Azure is light. The data can be accessed whenever the user desires. There are various career opportunities in the power of Azure Data Factory.

About the author

▷ Azure Data Factory Tutorial for Beginners (2023) (5)

▷ Azure Data Factory Tutorial for Beginners (2023) (6)

Anjaneyulu Naini

Anjaneyulu Naini works as a content contributor for Mindmajix. He has a great understanding of today's technology and statistical analysis environment, encompassing key aspects such as analysis of variance and software. He is proficient in various technologies such as Python, Artificial Intelligence, Oracle, Business Intelligence, Altrex, etc. Connect with himLinkedInAndTwitter.


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