The advance of emerging technologies enabled by cloud computing has been dizzying over the last several years.
In some cases, these new technologies have been created by cloud companies specifically for the cloud; for instance, serverless. In other cases, a technology has advanced by its close relationship with the cloud; for instance, machine learning and artificial intelligence.
In either case, these emerging technologies are changing not just cloud, but the larger world of enterprise computing – not to mention sectors ranging from retail to media to pharmaceutical.
Top Emerging Technologies in Cloud Computing
These emerging technologies – either cloud-based or highly interoperable with the cloud – offer enormous promise, yet they have also contributed to a growing complexity in cloud computing.
In the spring of 2014, container technology burst upon the scene; the tech world was abuzz with how containers make software development faster and more nimble.
Containers weren’t actually new, but a little known outfit named Docker made them easy to use.
Unlike the virtual machine popularized by VMware, which has to hold the entire OS, containers wrap a piece of software in a capsule that’s like a lightweight “computing suitcase.” The container carries the software itself and only the bare essentials needed (libraries and configuration files) to travel among computing environments.
Adoption has been fast for such a new technology. The Rightscale State of the Cloud 2019 report indicates that 66 percent of enterprises have adopted containers. Similarly, 60 percent have adopted Kubernetes, the container management system developed by Google.
Given the myriad elements of a cloud environment, it’s no surprise that it’s producing a wide variety of emerging technologies.
Prior to AWS’s introduction of serverless architecture in 2014, cloud customers estimated – guessed – what level of computing resources they’d need to provision, and paid accordingly. With serverless, customers are charged only for that they actually use.
More significant, with serverless, the cloud provider handles the infrastructure headaches of maintenance and scaling, making it easier and faster for customers (particularly developers) to build out their cloud-based systems.
Serverless, also known as function as a service, allows the cloud world to spin much faster and more efficiently.
Updating large, complex pieces of software can be a slow and cumbersome process. Enter microservices, which started gaining buzz around 2012.
Microservices breaks these unwieldy monolithic apps into a number of smaller, joined services, or “modules.” It uses a modular approach, with small teams updating modules as needed, independent of the full hulking application. (Anecdotally, industry lore says that the module should be small enough so a team that can be fed by two pizzas can update it.)
Microservices enables continuous delivery of freshly updated software. Like serverless, it allows app development to move at the faster speed necessitated by the cloud era.
Speaking of continuous delivery, DevOps is focused on exactly that: CI/CD ( continuous integration / continuous delivery). DevOps, which started gaining serious momentum around 2012, is as much of a cultural shift as a technology. Its aim is to speed software develop by getting two groups with very different worldviews to speak to one another: developers and operations managers.
Developers are often artists at heart; they create things. Operations managers are typically the opposite; they embrace metrics and spreadsheets. But if the dev team and the ops team can work together (hence, “DevOps”) the all-important software updates can flow out fast enough to gain competitive advantage.
Internet of Things (IoT)
In the cloud era, everything – everything – can be connected to the Internet. From your wristwatch (Fitbit, Apple watch) to your home controls (Nest) to self-driving cars to surveillance camera. This vast network of sensors – the Internet of Things – generates ginormous oceans of data.
IoT exists separately from cloud computing, but two factors inextricably link the two technologies.
First, as is true with many new technologies, with IoT businesses wondered: How are we going to buy it? We can’t build it all from scratch – it’s too expensive and complicated. And as is often the case, with IoT the answer is: through the cloud. Each major cloud platform offers an IoT solution.
Moreover, the key question about IoT, also known as “edge computing,” is: where will we process all that data? For many businesses, the answer is “in our cloud platform.” Cloud-based data analytics, powered by the cloud providers’ hyperscaling servers, offer superior data crunching.
Artificial intelligence is the big one, the emerging technology that will ultimately do the most to profoundly shape the future. With its promise of software that learns independent of human assistance, AI is the great tool whose august potential dwarfs all other tools.
And again, while AI certainly exists separately from cloud, AI is far too complex for businesses to build themselves. So businesses look to cloud companies for their AI solutions, including machine learning and deep learning tools.
In the early days of cloud, cloud’s ability to offer basic compute and storage was the great democratizer. Cloud providers enabled small-fry companies to “rent a datacenter” and so compete with big whales. As cloud matures, it is cloud-based AI that is enabling the next generation of under-funded visionaries to realize their vision – just like deep-pocketed outfits.