What is Azure Data Factory?

All around us, data is being collected. Have you ever considered how a vast volume of data may affect a company's decision to move its operations into a cloud environment? Do you have any questions about how it is feasible to handle data created in the cloud by referencing data from on-premises or other data sources? This is where we conceive about the Azure Data Factory.

Microsoft Azure has answered every question you've asked thus far. Microsoft Azure offers us a platform to design workflows that can input and consume data from various sources, including cloud and on-premise data storage. Once this data has been transformed, it may be stored in a database or processed using current computing services such as Hadoop. The outcomes of this processed data are subsequently released on a cloud data repository for the business intelligence (BI) apps to execute, known as Azure Data Factory.

What Is Azure Data Factory?

Azure Data Factory is described as a cloud-based solution for data integration that enables us to construct workflows for data-driven activities in the cloud. This aids in the automation and coordination of data transformation and data transport. Azure Data Factory does not store the data itself. Instead, it lets us monitor the processes for both programmatic and UI-based approaches.

Commonly Used Terms of the Azure Data Factory

• Triggers: A trigger indicates when a data pipeline should execute. There are several sorts of triggers in an Azure Data Factory. You may also plan a trigger at a time suitable to you. You may also indicate the characteristics of a trigger firing by utilizing a Data Window Trigger.
• Integration runtime: An integration runtime refers to a calculated infrastructure employed by Azure Data Factory.
• Pipelines: A pipeline refers to a series of comparable or independent operations. You may use this activity to either copy, do data transformation, or execute data flow in Microsoft Azure.

What Is the Working Method of the Data Factory?

The data factory enables a user to manage and develop data pipelines that further modify and transport the data and execute them according to a set timetable. Now let's discuss the three phases in the Azure Data Factory particularly.

• Collection and connection: The connection procedure relates to the needed data sources such as file sharing, FTP, online services, and Saas Services. This stage includes relocating the data required at a centralized place to process copy activities in a data pipeline.
• Transformation and enrichment: Once we have found the data in a centralized place in the cloud, we can change it by installing the services like HDInsight Hadoop, Data Lake Analytics, Spark, and Machine Learning.
• Publishing: Now, we can transport the converted data from the cloud to the on-premise sources like a database server. We may also maintain it in cloud storage to consume analytics tools like BI.

Activities for the Data Migration in the Data Factory

Utilizing the data factory, a user may accomplish the data migration between the on-premise data storage and a cloud store. The copy function of the data factory transfers all the data from the source of data to the destination storage.

Microsoft Azure offers several data stores such as Azure Blob storage, Azure Data Lake Store, Azure Cosmos DB, Oracle, Cassandra, and many more. You can also go through the azure cloud certification for further specifics on activities and data factories. Some transformation activities that azure provides are:

• Hive
• MapReduce
• Spark

These actions may be added to the data pipelines separately or in a chain. In rare circumstances, the copy feature of the data factory does not operate. In this situation, you may utilize the net custom function in the data factory by adding your logic of moving and copying data. 

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What Are the Four Major Components of a Data Factory?

• Datasets representing the data structures:  Any input dataset represents the input for the data pipeline activity. For instance, The Azure Blob dataset represents the blob container and a folder present in the storage of the Azure Blob, which allows the data pipeline to read the data.
• A pipeline is a group of specific activities: Data pipelines are used to group the activities into a small unit or a collective task. For example, a data factory can contain one or more pipelines. 
• Actions to perform on the data: The data factory currently supports two activities: data movement and data transformation.
• Inter-linked services can define the required information: For instance, Azure blob storage linked service can specify the connection to the Storage account.

Steps to Create a Data Factory

• Given below are the steps you need to follow to create a data factory:
• Take a Microsoft Azure subscription. 
• Sign in with a user name and define your role ( contributor, owner, or administrator ) before you start creating a data factory.
• Open the Microsoft Azure Portal in your default web browser.
• Search and type "Data Factory" in the search panel.
• Click on the Data Factories option.
• Click on the "+" option.
• Mention your subscription type.
• Mention the resource group.
• If you don't have a resource group, create a new one.
• Select the nearest Microsoft Azure region.
• Enter a unique name for the data factory.
• This setup will require you to configure a repository from the Git Configuration tab for the CI/CD process. 
• Mention if you want to use a Managed VNET for the ADF.
• Mention the endpoint to be used for the data factory.
• After this, click on the review option and then the create option. 
• You will also be able to see the progress of creating your data factory from the Azure Portal.
• A new window will appear when the Data Factory is created successfully.

Conclusion 

We hope that this article explained you everything about what is data factory in azure is. A Microsoft azure training can help you to learn more details of Microsoft Azure and the process of data pipelines.