Azure Data Factory

09 november 2020 om 10:00 by ParTech Media - Post a comment

Data generated by applications and products is increasing exponentially every single day. There is no doubt that data has turned out to be the most important element for all businesses in the digital age.

There’s a reason why most Fortune 500 companies are spending billions on Data Science and Machine learning. This is because everyone wants to leverage data to predict the future. Business owners and CEOs want data to make better decisions. When you have the right data, you can change the course of your business and transform it into something wildly successful. And this is where Azure Data Factory comes into play.

Table of Contents

  1. How will Azure Data Factory help in making better decisions?
  2. What were the different versions of Azure Data Factory?
  3. How to create an account in Azure Data Factory?
  4. Working Principles of Azure data Factory
  5. Benefits of Azure data Factory
  6. Verdict

How will Azure Data Factory help in making better decisions?

Collecting data and deriving results from them is hard. It becomes impossible in a cloud environment that has 1000’s of Gigabytes of data flowing in a millisecond. You need a reliable way to collect data, store it, and derive valuable results. Microsoft Azure Data Factory helps you collect data and transform it into measurable results. In even simple terms, Azure Data Factory does two things at scale -

  1. Collect data from various sources
  2. Transform data into information for businesses.

Part 1: Collect data from various sources

Azure Data Factory can collect data from various points like SaaS applications, data storage units (both on-premise and cloud units), and convert it into various formats before storing it in its server. This gives you total control over how the data is brought and stored.

Part 2: Transform data into information

Azure Data Factory transforms data into information from which assumptions and results can be derived. Azure Data Factory serves the complete package as it allows data collection and transformation in the same interface.

What were the different versions of Azure Data Factory?

The first version of the Azure data Factory was released on August 6th, 2015. At the time of its release, Azure Data Factory was a very simple tool for processing data.

If you take a look through its history, Azure data Factory V1 was nowhere near the SQL Server Integration Services (SSIS) available in the market. It had no dominance and no foreseeable way to beat its competitors in the cloud space.

But then, Microsoft Azure decided to launch a new version filled with all the gizmos.

Azure Data Factory V2 was released on September 25th, 2017 and its most important features were its ability to shift SSIS services to Azure. With Version 2, Azure Data Factory also introduced the concept of running a pipeline on schedule.

This was exactly when Azure Data Factory began to transform itself into one of the leaders in the market.

How to create an account in Azure Data Factory

  • Before we create an account in Azure Data Factory, you need to get yourself a subscription from Microsoft Azure. To do that click here.

  • You need to check your permissions. Make sure that you’re either the ‘Owner’ or a ‘Contributor’.

  • On your dashboard, navigate to the new Azure Data Factory Page.

  • Click on the option named ‘Create a resource’. Sometimes, this option would also be named ‘Create Data Factory’. Make sure you click on any one of the two options available.

  • Fill out the basic details like Region, Name, and the Version of Azure Data Factory (always choose V2 )

  • Under the ‘Git Configuration Tab’ select the checkbox named ‘Configure Git Later’

  • Leave the options in the ‘Networking’ and ‘Tags’ tab as they are.

  • Finally, click on ‘Review and Create’ to deploy your Azure Data Factory.

If you have followed the above steps, your Azure Data Factory must be deployed and ready for use by now.

Working principle of Azure Data Factory

We have divided the working principles of Azure Data Factory into the below subsets -

Input Dataset

The data that we have in our data store is called the input dataset. This is raw data and you cannot use it to obtain any results. However, you will pass this dataset through a pipeline and process it for results.

Data Transformation Pipeline

The pipeline works on your datasets and transforms it into information. The pipeline uses stored procedures or other formats like HIVE to transform data into valuable information.

Output Dataset

The output dataset contains information that has been processed in the pipeline. This is linked to on-premise and cloud storage units like Azure Data Lake or blob storage.

Linked Services

These are the storage units that store the information processed in the pipeline.

Gateway Bridge

The Gateway acts as a bridge and connects our on-premises data to cloud services like Microsoft Azure.

Cloud services

Cloud services and analytical software like Apache Spark, R, and Hadoop allow you to work on output datasets and view them in graphical formats. They help you obtain results and assumptions from the data that you have derived.

The Azure Data Factory allows you to create and automate data pipelines on a varying set of schedules (hourly, daily, weekly, etc.). This allows you to automate data transformation in your business. The datasets that are consumed by workflows comprise of time-sliced data points. We can change the mode of the pipeline and schedule it according to your needs.

Benefits of Azure Data Factory

  1. It is one of the most powerful cloud-based data integration services, that enables you to create cloud-based data-driven workflows at scale.
  2. It allows you to automate the movement and transformation of data in the pipeline
  3. Azure Data Factory combines as an ETL+ Transform and Load platform.
  4. The drag and drop interface is easy to manoeuvre and helps you amplify your productivity levels.

Verdict

If you have been thinking about using Azure Data Factory for some time now, it's time to go for it. It can save you hours and help you setup data transformation pipelines with ease.