Introduction to Azure Data Factory

Organizations often face a situation where their data generation from applications or products increases exponentially. As the data is generated from different products, it is difficult to analyze and store all of the data.

Azure Data Factory can help to manage such data. It stores all kinds of data with the help of data lake storage. You can then analyze the data and transform it using pipelines, and finally publish the organized data and visualize it with third-party applications, like Apache Spark or Hadoop

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What Is Azure Data Factory?

Azure Data Factory is a cloud-based integration service that orchestrates and automates the movement and transformation of data. It works heavily on the data that you store.

Let us discuss the process followed in the Azure Data Factory.


Input Datasets

This represents the collection of data within the data stores. The data passes through a pipeline for processing.

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A pipeline consists of a group of activities, such as: 

  • Data movement activity
  • Data transformation activity using:
    • SQL
    • Stored procedures 
    • Hive   

Output Datasets

After the data is transformed into the pipeline, we get an output dataset. Here, we get a structured form of data. 

Linked Services 

The data from output datasets passes to linked services, such as:

  • Azure Data Lake 
  • Block storage
  • SQL 

Linked services contain information needed to connect to external sources. This is similar to the concept of a connection string in an SQL Server, where you define the source and destination of your data. 


This connects your on-premises data to the cloud. It consists of a client agent that is installed on the on-premises data system, which then connects to the Azure data.


The data is analyzed and visualized using a number of analytical frameworks, like Apache Spark, R, Hadoop, and so on.

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What Is Azure Data Lake?

Azure Data Lake is a highly scalable, distributed, parallel file system in the cloud that is specifically designed to work with multiple analytics frameworks. 

The data in output datasets (collected from mobile, the web, social platforms, etc.) is sent into the Azure Data Lake Store. It is then provided to external frameworks, like R and Apache Spark. 

Data Lake works on two main concepts: storage and analytics. 


Storage is unlimited, allowing users to save very large files. A variety of data (like unstructured or structured data) can be stored here. 


Through analytics, you can monitor and diagnose real-time data from connected devices, such as vehicles, buildings, or machinery to initiate actions such as generating alerts, responding to events, and optimizing operations.

You can also monitor financials such as:

  • Financial transactions in real-time to detect fraudulent activity
  • The use of a credit card across geographic locations 
  • The number of transactions on a single credit card

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Cloud computing is no longer a new concept for individuals working in information technology. If you are aspiring to become a cloud engineer or you’d like to pursue a job role in cloud computing, understanding Microsoft Azure is an important building block. 

Besides Azure Data Factory, Azure has a lot to offer to its clients, and learning more about these products can broaden your cloud computing skillset. Simplilearn offers Microsoft Azure Fundamentals Training for those interested in gaining expertise in Microsoft Azure. Check out the course and you will be able to do the following: 

  • Design and implement web apps
  • Create and manage virtual machines
  • Design and implement cloud services
  • Design and implement a storage strategy
  • Manage application and network services

About the Author

Shyamli JhaShyamli Jha

Shyamli is a Senior Research Analyst at Simplilearn. She is proficient in Blockchain and Cryptocurrency, Cloud Computing, Android Development and other coding languages like C, C++ and Java.

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