Real-Time Technology Trends That Will Drive 2021 Innovation – RTInsights

Real-time technologies, methodologies, and solutions adopted in 2020 will enable business success and innovations in 2021.

The value of
real-time systems came into focus in 2020 as companies responded to the
disruptions due to COVID-19. Having to make quick adjustments drove the need
for timely insights into fast-changing conditions. As a result, the year saw
new attention paid to real-time systems and the underlying technologies that
make them possible.

Many of the changes and technologies adopted will be critical to success this year. So, looking back at how businesses addressed their pressing issues in 2020 with real-time technologies is really a look ahead into the methods and solutions that will enable business success and innovations in 2021. Here is a brief overview of the technologies and trends to keep an eye out for in 2021:

Programming
languages for real-time applications

Debates
about which programming language is best for a particular application are
legendary. Developers have strong opinions, and some languages have ardent
users. Three of our most viewed articles of the year dealt with this issue. Proponents
of Golang, Python, and R, three of the commonly used languages used for real-time
applications, weighed in.

Development
methodologies

No matter
which programming language is used, businesses had to find a way to develop
applications quickly to meet the changes brought on by COVID’s disruption.
Attention turned to development methodologies that considered speed to market, developer
resources, and the need to maintain applications over their entire lifetime. As
a result, DevOps and low-code/no-code programming played an increasingly
important role as businesses needed to rapidly develop, deploy, and update
applications to keep pace with ever-changing customer demands and market turbulence.

Architectural
considerations for real-time applications

Many businesses in 2020 embraced cloud-native, microservices approaches to real-time application development and deployment. The reason: Microservices help developers deliver new features more quickly and effectively than ever before to keep giving users what they demand. Such models use containers, which offer a way for processes and applications to be bundled and run. They can be used throughout an application’s lifecycle, and they allow large applications to be broken into smaller components and presented to other applications as microservices. The result is an explosion in the number of containers to manage, maintain, and scale over time. Enter Kubernetes. Kubernetes became the dominant solution for container orchestration in 2020 and will remain so in 2021.

Notable
application areas

Intelligent
edge: Last year saw
a perfect storm for the intelligent edge. The combination of new connectivity services
(particularly 5G), real-time analytics, the Internet of Things (IoT), and new
compute power (embedded and hardened systems, new GPUs, and more) brought
intelligent edge into the mainstream. Specifically, the edge expanded into a
greater and even more dominant part of the computing infrastructure equation. The
result: significant changes in the way information is shared and managed in
devices, sensors, and systems across the globe.

Digital
twins: Digital twins are finding broader use
and playing a more important role in innovation. Driving the demand and
interest in digital twins is the wealth of data available from smart sensors
and IoT devices. Such data creates an opportunity to perform intelligent real-time
monitoring to dramatically improve responsiveness and situational awareness.
Applications of digital twin technology are wide-ranging, including everything
from a jet engine, a human heart, and an entire city. All these entities can
have a digital twin that mirrors the same physical and biological properties as
the real thing.

Safe
return to work: As
companies begin to envision a new normal, important office and real estate
decisions must be considered so employees can return to work safely. What’s
needed is an accurate and real-time picture of company data. In the
post-pandemic new normal, AI-driven intelligence will enable company management
to dynamically assess the operational costs, security risks, and productivity
trade-offs associated with having employees return to work in their offices.