- [Instructor] So now let's move
from the storage level to the data level.
And understanding the difference is important.
So what's important here is that we understand
that we're moving up from the ability
to store data to the ability to manage data.
So ultimately this is about our ability
to not only store the data physically,
but the ability to manage the data
in some sort of a meaningful way.
We're able to call through the data,
we're able to find the information we need,
and we're able to do very sophisticated things,
binding it to machine learning,
binding it to big data systems.
So why is the data needed and how
is it going to be used within the enterprise,
ultimately that dictates the way
in which you do the architecture.
This is not about you creating net new things
of the business and ensuring that you're backing
the proper technology into the requirements.
So within cloud architecture, again,
processing level, data level, and storage level.
We're going to break down data level this time.
We have both transactional type systems,
such as updating a sales order entry system,
you know from people in a call center
where they need to have access to information,
that's constantly being updated,
and you may have tens of thousands of users
that are updating a single database at the same time,
with transactional data, basically recording a sale.
Or recording a service level agreement
or recording something that is an instance of a business
And then we have big data analytics
understanding and intelligent abstractions of the data,
and we'll do this in essence to take
the transactional data in many instances,
and then move it up into an aggregated database
which allows us to slice it in different ways,
to find out how the business is doing.
For instance, if we did transactional systems
in terms of sales order, we need to figure out
how sales have occurred for the last five years.
What pattern is really showing up within the data,
are we selling more in the summer versus the winter.
And we're able to ask very detailed
and sophisticated questions to the information,
and it's able to tell us.
So breaking data level down another level,
we have transaction data, big data, analytics,
as we mentioned prior.
And then we have a couple of options
in terms of technology.
We have relational and object based databases
which can be transactional systems,
and by the way, there's other types
of database models as well but these are the primary ones
that you'll deal with as an architectural pattern.
So relational databases such as MySQL, Oracle,
Amazon's relational database service, RDS,
and the ability to in essence use traditional
relational database, are new cloud native databases,
which are relational in nature,
to allow you to basically solve the relational issue.
Or object based databases which are able
to in essence remove this detailed and static structure
that relational databases provide,
which allow you to store data under a different model.
But also retrieve the information as needed,
to in essence bind them to an application,
bind them to decision support system,
all these things are possible.
So under transactional you have relational and object,
but there are many other kinds of databases
that you find in cloud competing as well.
And then big data analytics we have the abilities
of leverage data warehouse type databases,
you know, such as Amazon web services red shift
which is a columnar database,
which allows you to store a great amount of information,
and allows you to in essence drive those information
resources into a way in which you can call through them,
on demand, to find out what the data means.
Not what the data is, but what the collection of data
actually means to the business.
And then finally machine learning, artificial intelligence,
deep learning, all these other systems,
can be bound to the database as well,
and they provide you with the ability to learn on demand,
and so in other words, I am able to gather petabytes
of information but as far as understanding
the meaning of the information and the ability
to in essence gain knowledge patterns from the data,
we may bind that to a machine learning system,
and the machine learning system will be powered
by the data to allow you to make assertions
as to what the data means.
So breaking it down one more, we have under relational,
we have Aurora, MySQL, are options that are there,
and also Oracle which is a traditional database,
which runs in the cloud.
Aurora and MySQL are kind of one and the same,
but they are able to run on any number
of different puppet cloud providers,
Aurora is an AWS function, it's basically a version of MySQL
or Oracle is Oracle, it's a work relational database,
and you can certainly transfer your license
and run them in the cloud.
And then for data warehousing we have red shift
as I mentioned earlier.
The ability to in essence leverage a columnar database,
to store and retrieve information,
vast amounts of information, and be able
to use that information in any number of ways,
it would provide you the power of doing that.
The power of big data.