In this article we will tell you about the key trends you need to know about cloud computing. The 5 V’s of Big Data was recently updated to 7, with variability and visualization joining the original five: volume, velocity, variety, veracity, and value. This increase is a tacit acknowledgment that data is not only becoming more expansive but also more complicated.
Key trends you need to know about cloud computing in 2021
The five key data trends for 2021 will be artificial intelligence, cloud containers, data democracy, as well as perimeter and serverless computing. All of these trends were hit hard by the pandemic in 2020 and, in many ways, all of these technologies move in tandem; AI uses containers, which work well serverless, which helps democratize data.
Once the pandemic hit and companies around the world were forced to provide work-from-home capabilities, these trends proved to be critical to keeping business running as usual. All of these trends will continue to flourish for years to come. They are not momentary successes; they are sophisticated, business-altering technologies that all executives should be aware of and continue to apply.
Artificial Intelligence
In 2021, the cloud will help AI realize even more of its abundant potential. It may not reach the heights of hype that many have promised, but the massive amounts of data flowing to and through the cloud will help turn the promise into reality. AI is a difficult technology to implement, but the cloud and software such as containers, Kubernetes, serverless computing, and powerful ML frameworks will help users create more responsive and scalable AI.
Over the past few decades, many key cloud-enabled advances helped elevate AI from a floundering technology to one of nearly limitless potential. These include the emergence of affordable parallel processing, Big Data, and its 7Vs, as well as access to improved ML algorithms from companies like Google, Microsoft, and Facebook. Because of their “compile once, deploy anytime, anywhere” capabilities, cloud containers help facilitate the development and deployment of AI applications, which, in turn, democratizes AI.
Containers
Containers are an executable unit of software consisting of packaged application code along with all the necessary software libraries and dependencies that run it. Containers are self-contained units that include everything needed for them to run and can run anywhere, whether on the desktop, within traditional IT, or in the cloud.
Gartner believes that containers are the preferred way to package machine learning models, which can be used from other external applications without any coding requirements. Containers can include the entire machine learning process. They can scale as needed and spin up in minutes. During ML training phases, containers can use multiple host servers, and then the trained models can be distributed to multiple container endpoints and deployed where needed.
Although similar to a virtual machine (VM), containers do not virtualize the underlying hardware, only the operating system, as well as the necessary libraries and dependencies. This helps keep containers lightweight, fast, and highly portable. Containers also support modern development and architecture, such as DevOps, serverless computing, and microservices.
Data democratization
For today’s companies, data has become almost ubiquitous. “Visualization” is one of the additions in the face of Big Data, but its late addition should not be interpreted as a lack of importance, quite the contrary. It is perhaps one of the most important of the 7 V’s. Cost-effective BI tools like or IBM Cognos are gaining traction in enterprises, both large and small, and data visualization is one of the best ways to extract value from them.
By 2021, more IT departments will relinquish power over their IT tools and software, democratizing more data. It won’t just be business intelligence tools, but also data integration tools such as Microsoft SQL Server Integration Services, Alteryx, or RapidMiner. The business of self-service analytics tools will continue to grow. The democratization of data will enable employees at all levels of an enterprise to explore and analyze data on their desktops, their mobile devices, and almost anywhere.
Edge Computing
Most data comes with an expiration date and this is the theory behind edge computing. Why capture data on an edge device, send it to the cloud, build the models in software up there, compile the results and then send it back to the edge device that initially captured the data, which then uses it to send an alert. The edge device should be part of a marketing system. Why not have the edge device also build the models? With hardware getting smaller and smaller and software getting much more sophisticated, highly complex models can be included in an edge device, at the edge of the cloud, making the data much more useful and action-oriented.
Large vendors, such as AWS, Dell, HPE, Google, IBM, and Microsoft, are adopting an edge cloud strategy that leverages serverless computing models. Data can flow data through real-time applications at the edge of the cloud, directly to the consumer’s mobile device. IoT solutions can now be deployed almost anywhere, and cloud providers are adding edge computing services to assist with content delivery across hundreds of thousands of local points of presence.
2021 will also see an increased emphasis on enterprise network edge security and the protection of users, services, applications, and data as enterprises adopt distributed application environments.
Serverless computing
Serverless computing allows developers to do what they do best: write code. Cloud providers are responsible for setting up and maintaining the infrastructure and servers that run that code, along with the maintenance required to ensure that the systems work properly. In 2018, Gartner highlighted serverless computing as one of its top ten computing trends for infrastructure and operations, and time has proven Gartner’s prediction correct.
Serverless computing integrates Backend as a Service (BaaS) capabilities and the cloud provider handles all system infrastructure management, operations and maintenance costs, security, and software patches and updates while allowing customers to focus only on building applications.
