In 2014, Google announced it was embracing Docker and open-sourcing a new tool to manage computing workloads and cover large scale computing infrastructure. At the time, this was hailed as revolutionary….it was believed to be the “secret weapon” in cloud computing. Now, we know this tool as Kubernetes. Imagine you have several applications, packaged in several containers. These are running across a fleet of different computers in data centres and cloud environments. In this scenario, who is going to tell which container where to run? How are these different forms of hardware going to function as one unified computer to run your workload? In the business, this is known as Orchestration, and Kubernetes can do this for you. Here some key trends that cloud-native enthusiasts need to know about:

1. 4 Trends Shaping
the Cloud-Native
World in 2020
PRESENTED BY TYRONE SYSTEMS

2. In a poll by Stackrox, a cybersecurity
company, 2019 proved itself to be the
year Kubernetes became dominant for
container orchestration: 86% of
respondents used it to orchestrate their
workloads, up from 57% in 2018.
What remains missing from this picture,
though, is what proportion of workloads
are not run in containers and remain in
traditional architectures.
KUBERNETES GOING DEEP INTO
ENTERPRISES

3. It was clear from the beginning that
Kubernetes would be a multi-cloud thing,
running across public cloud providers.
What was less clear was whether hybrid
cloud would be on the menu, natively.
The answer relied on the cloud
hyperscalers: Would they choose to
embrace hybrid cloud and make their
offers compatible with on-premise
Kubernetes solutions?
HYBRID CLOUD

4. CLOUD-NATIVE SECURITY
Both the large scale adoption of
Kubernetes in enterprise and across
multiple environments means that
Kubernetes is being placed under a lot
of pressure in terms of security.
Migrating production-grade workloads
requires a very different level of security.

5. THE EDGE COMPUTING
In a world where a car or a fridge is a
computer, too, and the volume of data
transfer increases, why not run
computations close to the data instead
of doing it all in data centers?
Running some compute at the edge
and then centralizing the results would
reduce bandwidth requirements,
increase security and privacy, and
optimize compute use.

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