2.5 quintillion bytes of data is being produced every day. This is the volume of data being produced by half of the population of the world, as the other half still doesn’t have access to the internet yet. However, with companies like Microsoft, Google, and Facebook planning to establish more datacenters and lay cables that connect the entire world, that volume of data is bound to increase exponentially. This data is worth its value in gold, especially if processed into meaningful information. In this Microsoft Azure training article, we will talk about processing data with Microsoft Azure.

You can process and manage your data with Azure Data Factory, which facilitates hybrid data integration. Other than this, you can build your data pipelines with a graphical user interface and can turn your raw data into custom applications.

SQL Server Integration Services Package Execution

Managing execution environment and working on SQL Server Integration Services (SSIS) has been made easy by Azure. Now all you have to do to lower TCO and get better scalability is to lift the SQL Server Integration Services packages.

Code-Free Data

Now you can easily improve your TCO connectors across points of presence all around the globe. This includes Oracle, DB2, SAP HANA, Google BigQuery, Mongo DB and AWS S3 and Redshift.

Code-Free Drag and Drop Feature

Azure data factory helps in increasing productivity, which helps in finishing projects on time. Data integration has never been so easy, as now you can easily manage it with the code-free drag and drop interface. In order to get a smooth development workflow, all you have to do is connect the Git repository with the tool.

Multiple Language Support

No need to sweat over writing codes, as now you can easily do so in ARM, Python, and .NET. You can pick from the list of services and arrange them into data pipelines in order to use the most suitable tool for the task at hand.

Comprehensive Control Flow

Now you can use the extensive control-flow constructs for on-demand executions, and scheduling.

Data Integration

Step 1: Using Built-In Connectors To Access Data

You can move your data from on-premises to a cloud based storage.

Step 2: Writing Code or Building Codeless Data Flow

In this step, you will transform big data processing with the visual interface.

Step 3: Running the Pipelines

You can run the pipelines with trigger based scheduling and can easily monitor and track issues in pipeline activity.

Things You Can Do With Azure Data Factory

Here’s what you can do with your Azure Data Factory:

  • Revolutionize your data warehouse
  • Make modern data-driven applications
  • SSIS package execution in Azure

This is just an overview of processing data with MS Azure. If you want to get an in-depth understanding of the topic, it is recommended that you enroll in Microsoft Azure training with QuickStart.