Some times data sets get so large that the laptops we use freeze up. One way around this challenge is using Virtual Machines (VM's) in a cloud environment

A VM in the cloud is like having an extra computer that you can customize and access remotely, running on the powerful infrastructure of a cloud provider
such as Google (GCP), Amazon (AWS) or Microsoft (Azure).

VMs on the cloud enables analysts, data scientists, and data engineers to work more efficiently, scale their operations, collaborate seamlessly, and maintain a high level of security and compliance.

A simplified traditional technical infrastructure for an end user may look like this:



This kind of architecture is suitable for many use cases, provided the data size is manageable and the analysis is not overly complex.

As data sets grow larger and more complex, the time required to transfer data to your local computer for processing can increase dramatically.
In some situations downloading and uploading data can take many hours, followed by extensive time required to perform analysis.
This can cause a laptop to slow down significantly, rendering it almost unusable for other tasks.

The solution to this challenge is leveraging additional computing power from the cloud.

In the cloud it is possible to add compute power or storage on an as needed basis, while only paying for the resources that you need.




Workloads that overwhelm a laptop can be easily handled in the cloud, freeing up the user's laptop for other tasks.



Setting up a cloud environment can significantly benefit analysts, data scientists, and data engineers, enabling them to deliver results more quickly.