Over the past couple of decades, data science has become increasingly important with applications in areas such as big data and machine learning. The increasingly complex data sets have required data scientists to find new and better tools. While searching for ways to improve efficiency, cloud computing, specifically Microsoft Azure, should be on the radar of every data scientist. This platform presents benefits that make scientific analysis easier in addition to creating new ways of manipulating and analyzing data.
Using Cloud Computing for Data Science
One of the big challenges modern data science faces is the amount of data being created. Modern computers, sensors, and other data collection technologies have advanced to the point where they can generate terabytes of data (or more) every hour. Human organization and analysis of that data is unfeasible; therefore, the right computing tools are necessary.
Cloud computing provides one of the best value propositions for data scientists: it’s accessible, scalable, cost-efficient, and powerful. Public cloud services such as Microsoft Azure, AWS and Google Cloud Platform offer easy access to all the computing resources that data scientists may need.
The Benefits of the Cloud
The cloud is nothing more than a computing resource that is available through the internet. Thus, many benefits of the cloud can theoretically be replicated with on-premises equipment. Nonetheless, there are several key advantages where this is impractical or unfeasible:
- Easy To Use: Compared to setting up and provisioning an on-premises server, public cloud instances are very easy to use. Data scientists don’t need to be IT experts to be able to increase the storage in their systems or spin out a new server instance to run an analysis. Of course, scientists should always be supported by IT professionals, but the cloud makes a self-serve model easier.
- No Physical Storage Requirements: Local computers may take up a lot of space. This is especially true if you need to accommodate for extensive storage requirements of modern data science. By offloading the storage needs to the cloud, you can avoid the cost, maintenance, and other challenges that accompany keeping on-premises infrastructure.
- High Data Availability: If you are using one of the Big Three public clouds, you can be confident that your data will be available to your team members no matter where they are in the world. Team members can collaborate from around the globe without missing a beat thanks to powerful networks in modern cloud computing.
- Scalability: Cloud services can typically be scaled with a few clicks of a button and a few minutes of work. On-premises equipment cannot be scaled nearly as efficiently.
Why Choose Microsoft Azure
Of course, the above benefits are true of all major cloud computing solutions. Thus, you may be wondering why you should opt for Microsoft Azure. These are a few reasons to consider Azure specifically:
- Pricing: Compared to AWS, solutions such as Windows Server and SQL Server in Azure are much less expensive. In general, a data science team can perform all the same work using Azure more cost-efficiently than would be possible on AWS.
- Microsoft Optimization: Most teams are using Microsoft tools such as the Office suite, SharePoint and Access. Many teams are also using Windows as their operating system. It is easier to build a strong infrastructure for these tools on Azure than any other cloud service.
- Security: Google, Amazon and Microsoft all take security seriously. However, Microsoft has made significant progress in this area, earning more compliance certifications than their competitors and proving they have made a more serious commitment to privacy. Microsoft has also been in the enterprise solutions business for more than 30 years (longer than Google or Amazon have even existed).
Learn More About Cloud Computing in Data Science
Data science is a growing field that promises to offer great opportunities for both scientists and technologists. Discover more about how cloud computing courses can support success in a data science career.