Lots of businesses consider migrating their infrastructure to the cloud or using hybrid cloud solutions. This article focuses on describing what are the main features of cloud computing and what are the advantages of migrating business systems to the cloud. 

What is cloud computing? 

Cloud computing is a group of services that focus on providing businesses with ready-to-use, immediately deployable, and customizable IT infrastructure that can be used for data storage, data processing, data analysis, machine learning, and to maintain live systems that the company needs to function (i.e. warehouse data or live, on-line scoring APIs). It is a constantly growing business that substitutes or replicates the on-premise infrastructure.

Infrastructure maintenance in cloud computing services

What is most important here, the cloud provider (Google Cloud Platform or Microsoft Azure for example) is responsible for all the maintenance of the infrastructure – the business does not have to concern about buying machines, servers, maintaining networking, and so on – everything on the infrastructure side is maintained by the cloud provider. The cloud storage provider also provides the customer with a guarantee that the data will be stored safely (protection against data loss) and that the processes will be running properly. The customer’s role in this is to define their business needs and requirements, and during cooperation with the cloud provider, proper configuration is set up in order to meet the customer’s needs. 

Cost management and pricing in cloud computing

Cost management and cost efficiency is also another major point of cloud computing – the customer has immediate and precise information on the costs of starting and maintaining a cloud computing infrastructure and can monitor their costs almost in real time. They pay only for the used infrastructure and do not incur the costs of purchasing servers and machines. The costs can then be split among projects, to see which project or department uses most of the infrastructure. Cost reporting in that case is way easier and manageable. 

Resources flexibility in cloud computing

Let’s consider a simple scenario: you want to perform a data analysis that requires lots of CPU and RAM, and all your on-premise servers lack the capability to do so currently (whether it’s because of current usage, or in general available computing power). In this case, you are forced to depend on your “hard” infrastructure and wait for your turn for the resources, or purchase a more powerful machine, which computing power will be used only for a short time. When the machine is idle, it is literally a waste of money. 

When your infrastructure is cloud-based, there is no such dilemma: in a time of 5 minutes you are able to increase your computing power, for example, increase RAM of your virtual machine from 8 to 128 GB, and on the same scale, to increase your available CPU power (i.e. increase the total number of cores). What is also important – you can do that on an already deployed machine – you will not lose your operating system and all the installed frameworks and packages. 

Your analysis then will be possible to perform and will perform much faster. What is also important here, this change can be temporary, that is, you can increase your computing power only for the time of the calculations, and then decrease it in order to reduce your costs. This provides both cost-effectivity and time-effectivity – instead of waiting for the results or wasting time fixing bugs and rerunning the same calculation many times, you can reduce the time of the calculation to the minimum and focus more on inspecting the results or developing the process.

Machine learning in the cloud

Another important topic is setting up machine learning environments for your analysis. Usually, in on-premise setups, it takes a long time to set up a system that contains all the necessary software, packages, frameworks that enable to perform machine learning. 

Cloud computing vendors provide us with ready-to-use machine images, that can be customized or tweaked. This skips the whole setup process because, in the cloud, the setup usually takes only a dozen minutes, and the machine is ready to be used by an analyst or a data scientist. 

If any customizations are made to the environment (i.e. custom packages), it can be then easily replicated, for example, if a new joiner arrives. This helps your business again to focus on the analysis process, and the results, rather than setting up infrastructure and fighting with technical problems. 

This also reduces the costs of on-premise machines. Since the data storage, data processing, and calculations are performed in the cloud, there is no need to purchase a high-performance laptop for the analyst – you can focus on the work environment because the computing power is in the cloud, available whenever you need it.

Cloud consulting

A lot of companies when setting up their infrastructure lack expertise and depend on external companies that provide them with infrastructural services. In on-premise solutions, usually, the solution is tailored to the company. This may be an advantage, but this solution lacks flexibility. And usually, any new modification takes time and cost. 

In case of cloud computing, the experience of cloud computing vendors provides us with lots of ready solutions, and the support of their consultants, which can help your company to pick the best products for your use case, and help you during the onboarding and development process. 

DS Stream’s expertise in cloud consulting

DS Stream is a company that encompasses both the cloud computing infrastructure expertise and analytical background. Our team contains specialists in picking, setting up, and maintaining cloud solutions for your business needs, and tailoring them to your requirements. 

We will help you in selecting the proper vendor that provides you with the cloud computing  services that you need, and then assist you in transitioning your business systems to the cloud. We will also provide you with expertise in machine learning development, our data scientists and data engineers will help you in creating proper data flows and creating complex machine learning processes. 

Feel free to contact us at any time if you are considering moving your business processes to the cloud.