Understanding Microsoft Azure’s Cloud Architecture

Transitioning data to cloud platforms is becoming a necessity for organizations of all sizes and industries. But for those just beginning their modern analytics journey, navigating the cloud platform marketplace can be difficult. Having a working knowledge of one of the leading cloud platform providers can streamline these efforts and get you to where you want to be.

In this demo, we dive into Microsoft Azure to discover the powerful tools that can be used to address your data analytics needs. We’ll walk you through a sample cloud data pipeline, beginning with the extraction of data from four tables in a SQL server database, loading it into Azure Blob Storage, and transitioning it into Azure Synapse Analytics where it can be leveraged by data visualization tools.

By applying lessons learned from this demo, you’ll be able to:

  • Solidify a reference architecture that can be used across all cloud engagements
  • Take traditional data and analytics skills and translate them to the cloud
  • Raise awareness and engagement in the cloud analytics space
  • Train and equip your team as you move to the cloud

Exploring Azure Cloud Architecture

Azure Cloud Architecture
Microsoft Azure is one of the three largest cloud platforms, along with Amazon Web Services and Google Cloud Platform. Want to see how Azure cloud architecture compares to Google Cloud Platform and AWS?


Azure Cloud Data Pipeline

The graphic above represents a sample AWS cloud data pipeline through which data is processed and moved between different storage and analytics services at specified intervals. Let’s take a closer look at the steps through which the data flows through this pipeline:

  1. Data Sourcing – The data begins its journey from a relational database management system (RDBMS), or in this case, the SQL server database in Azure Data Studio (similar to SQL Server Management Studio and Visual Studio Code Editor).
  2. Data Orchestration – We then use Azure Data Factory, which copies the source data and transfer it into a data lake as a zipped .csv file.
  3. Data Staging – The data is then staged in a data store or data lake. In this case, the data is staged in Azure Blob Storage, a general-purpose, scalable object repository that allows you to store structured and unstructured data in a centralized location.
  4. Data Orchestration – Once loaded into Blob Storage, Azure Data Factory moves the data into a cloud data warehouse (CDW).
  5. Cloud Data Warehousing – The data is then stored in Azure Synapse Analytics (formerly Azure SQL Data Warehouse) to be used as the source for all reporting and analytics activities.
  6. Data Visualization – Now that your data is transformed, it can then be consumed by any number of visualization tools, including LookerPower BIQuickSight, and Tableau.

Advantages of Microsoft Azure

One of the most flexible and scalable cloud platforms in the data analytics market, Microsoft Azure and its associated tools provide several advantages to help you on your modern analytics journey, including:

  • Effortless SQL ManagementAzure Data Studio allows you to execute SQL work with a modern, keyboard-focused SQL coding experience that makes your everyday tasks easier.
  • Flexible Data Storage – While Azure Blob Storage is optional for smaller, less complicated data sets, most companies can benefit from using Azure Data Lake for archival strategies, retention policies, and exploratory data science.
  • Streamlined System Setup – Azure software running in Microsoft Edge or other major browsers simplifies the overall system setup and alleviates the need for major environment installations.
  • Multiple Data Processing Options – In Azure Data Factory, you can process data by using Azure Databricks with Java or Python, or by using Azure HDInsight with Apache Hive or MapReduce, or by creating machine learning pipelines directly inside the app.
  • Simplified Data Lake BrowsingAzure Storage Explorer allows you to browse data lake files through any device or platform.


How to Select a Cloud Platform

Having your data in the cloud allows your organization to move toward more modern, self-service analytics and make data-driven decisions. However, the modern analytics landscape can be confusing, and many people are still trying to figure out where to start. For those in the beginning stages, it’s essential to understand the differences between legacy and modern analytics, the advantages of cloud architectures and other available platforms, and the next steps you should pursue. To learn more about preparing for a cloud analytics transformation, download our eBook, Modern Analytics: Reaching Beyond the Clouds.

When exploring different cloud platforms, it’s important to perform a thorough side-by-side comparison of other providers that allows you to measure against organizational criteria to better understand what options are available, what they offer, and how they can best suit your company’s needs.

Even with a thorough comparison and review of your organization’s guidelines, it can be hard to determine which cloud analytics platform will serve you best. If you need guidance or recommendations, our team of experts can help you develop and deploy a modern cloud analytics plan that will work best for your company. When you’re ready, contact us, and we’ll get you on your way.



Sense Corp is a leading professional services firm transforming organizations for the digital era. We help clients solve their toughest challenges by bridging the gap between what is and what’s possible.


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