Let's wrap up this first part of the Concepts, Architecture and Design
course for the CCSP with a few of the other things we need to consider
for the cloud. For example, the cloud is a shared facility in most cases,
and that means we have to make sure we have things like interoperability,
but also portability.
We can move to different cloud providers. We can move our data
around and hopefully in a seamless manner.
That means also we have reversibility to be able to go back
when necessary, but also good governance.
Something we'll look at in module 4 when we're taking a look at
evaluating cloud service providers. We need to make sure we have the
levels of availability to support our business needs.
That means resilience in case of some type of network or hardware failure.
We certainly have to consider security in the way we deploy the
cloud, and that, of course, includes the protection of privacy and
personally identifiable information.
We should also be able to assess and audit our cloud services to make sure
that we're getting what we're paying for, and as a cloud service provider
that I'm providing what is termed in the contract.
The cloud, of course, has to interface with many different technologies today.
And, of course, a lot of these are computational intensive tasks,
things we used to talk about grid computing to solve very large
mathematical challenges, for example. And a lot of these require a
large storage and data analysis processing capability, and the
cloud has really helped with that.
We mentioned grid computing was used to try to solve the human
genome and DNA, and that was done through a lot of independent
computers, but now we quite often using a cloud service can bring
that all into one location.
We also see this has been used in some of the Blockchain
calculations and cryptocurrencies, and, of course, in the
development of artificial intelligence.
And we see how we're able to very much improve the way we
can do data analysis and from that,
extract out the metadata and knowledge from the data
through vast amounts of data processing.
This, of course, helps us to improve on machine learning,
so our systems become more adaptive to changing situations
and more responsive as well.
When we look at cloud‑enabled systems,
we see a lot has moved now from just large central data
centers out to things like edge computing.
We have the integration of many things besides just desktops and
laptops and the whole Internet of Things. And this, of course,
allows many of these systems to be able to share data in a way that
is far less expensive and in many cases far more effective than it
was ever possible before.
We also have seen that a lot of new development has been enhanced
through the use of cloud services. For example,
I can create new applications and collaborative work teams using things like
DevOps or Dev development security operations as well.
We've seen the talk about quantum computing and the ability to be able to
integrate faster and faster processes into our systems as well.
In summary, in this module, the first module here of this course,
we've looked at some of the core principles of cloud architecture and
various types of service models. But we know that the way we deploy them
will be different according to our organization.
So upon these various alternative models,
the consumer can deploy cloud‑based systems that are
responsive to their individual business needs.