Our primary use case involves engaging with users to gather their requirements. Once we have a thorough understanding of their existing application, we document their needs and proceed to modernize it using the latest technology, .NET Core. If migrating the application isn't feasible, we start from scratch while ensuring we preserve the user's business requirements. Following this, we carry out the development and testing phases. Once the user approves, we proceed with pilot production testing before making the application available online. This step-by-step process is executed in adherence to agile methodology, enabling us to expedite the entire workflow. Throughout the migration process, we sought assistance from Microsoft, particularly for our first-time experience migrating from an on-premises client environment to the cloud. 

So far, we have successfully migrated application servers, web servers, and SQL servers from on-premises to the cloud. This transition has been a valuable learning experience for us. Notably, in the cloud environment, we utilize dynamic IP and refer to servers by their respective names rather than IP addresses. We also implemented the same security measures on the cloud as we had in the on-premises setup. These efforts have resulted in improved performance on the cloud, making our users satisfied with the upgrade. 

Presently, I am involved in projects related to the CLS content management system for my client. They currently utilize their own tool called E360, which operates as an application server. However, this tool relies on outdated technology. Hence, our focus is on gathering requirements and migrating it to the cloud, while also incorporating additional features. As we complete the requirement-gathering phase, our next steps include development and subsequent testing, including society and education testing. Following the completion of these stages, the application will undergo user acceptance testing. This is our current progress.

Microsoft Azure Data Box is designed to handle large amounts of data effectively, just like Snowball. I have been using it for edge solutions, especially for IoT applications.  

I am currently working with a client on utilizing the Data Box for extensive IoT data, particularly real-time data. It plays a crucial role in shipping the data back to the Azure Data Center.

We use the solution to migrate on-premise data and move legacy applications to the cloud.

The primary function of the solution is to migrate large amounts of data off-site and into the cloud.

We're looking at using this solution for some of our big data platforms. For example, creating data lakes and things like that.

There are a variety of use cases for Microsoft Azure Data Box. If you are a Microsoft shop, you do not want to work with glue, so that is why you would go with Microsoft Azure Data Box. If you are in a non-Microsoft environment, you don't want to work with a Microsoft product. But, it does provide a solution that integrates non-Microsoft products, both in AWS and Azure. At the end of the day, you do not want to pay for extra data storage. You do not want to pay both for AWS and Azure because you are going to need to replicate data across two cloud bases. It is all cost-based.

We are a solution provider I have worked on two different projects with the Azure Data Box. It was used to back up all of the data from different teams for the purpose of DLP. We backed up more than 400 terabytes in total.