-: In this video,
I am going to talk to you about designing analytical stores.
So what we're going to be taking a look at
is how do we choose an analytical data store in Azure?
And with that,
we're going to be looking at some key criteria,
exploring options,
and looking at the capability matrix to help us
to figure out how we choose analytical stores.
So with that, let's dive in and get started.
So the first thing is exploring the options.
And let me tell you, there are a lot of options.
So what you need to understand is to think
through questions that can help
you to narrow down these options into the one
or two that would make the most for you.
I do want you to be aware of this list, though,
and also keep in mind for the DP-203.
There's more on this list since in the DP-203.
For instance, we don't cover Cosmos DB on the DP-203.
And we don't cover Azure SQL Database on the DP-203,
but you need to be aware of these types of services
because that's going to help you in your career.
And that's also going to help you to narrow down options
when you're looking at questions on the exam.
Alright, so with that,
what are the questions that we need to know
to help us to choose?
Well, the first one is relational versus non-relational.
This is a huge question.
And if we're looking at a relational database,
we're looking at things that are SQL, right?
So Azure SQL Database or things like that, right?
If we are looking for non-relational, well,
now we're looking down the path of Cosmos DB
or something similar to that
that also helps us in our storage options as well.
So relational versus non-relational is definitely
the place that you could still start.
And just a pro tip.
As you are thinking about how to build the database,
don't just think about the project that you're on.
Think about how that project
is going to look in 3 or 5 years.
So what kinds of data is going to go in there?
What kinds of groups
are going to be interacting with that data?
How are they going to be interacting with the data?
Is it going to be marketing experts that have
no technical background trying to do something
or is it only going to be database architects?
So that's going to help you to kind of narrow down
that relational versus non-relational
and where your database is going to be heading.
Next, what type of database model do you need?
So we could do key value or document,
or column, or graph, right?
There's quite a few different database models.
So think about what you're going to have.
And a lot of that is going to be based upon
what you currently have.
So if you have a legacy system,
trying to change that system may not be the correct route.
A better use might be looking at what you have
and figuring out how you can carry that forward
or expand upon it.
Or you may need to scrap the entire thing,
but understanding the database model
that you're going to want is also a critical factor
in deciding your store.
So what other services
or activities are going to be
in your data engineering projects and pipelines?
So are we doing large-scale transformations in Databricks?
Are we tying in a bunch of MongoDBs?
If we are doing things like that,
that may skew us more towards, "hey,
Cosmos DB because you can use Mongo".
Or if we're using Databricks, okay,
that's going to help us to know
as well what kind of size we have.
Or Azure Synapse might be a good solution as well, right?
So that kind of helps us to figure out
what we need as we look at the other services
in the pipeline.
And then finally,
what kind of scalability needs do you have?
So again,
how is this database
or this store going to grow over time?
And that's going to be both in processing
or queries coming into it.
That's also going to be in size as well.
So think about both of those things.
So there's one more thing that we need
to look at before we wrap up this lesson.
And that is the capability matrix,
which if anything is going to be the most likely
thing that you would see on the DP-203.
So let's jump over and take a look at that.
And I'm going to throw a link in the video description.
So make sure you take a look at that.
This is under choosing an analytical data store.
There's a capability matrix here
that I would highly suggest
that you take some time to read through.
So plan on taking the next 10 minutes
or so after this video
and just kind of look through this.
This is going to give you a list
of all of those different resources or options
and it's going to break it down by category.
So you can see there's general,
there's scalability capabilities,
and then security capabilities as well.
So I don't think you should memorize this.
In fact, I'm going to say don't memorize this for the DP-203
or even for your job.
You can always go to the link.
But what I would suggest you do is, again,
take about 10 minutes or so, look through this,
and spend a little bit of time
just kind of getting familiar with what's on here.
And a few of the main stays
such as Synapse here for the DP-203.
Lastly, let's talk about some key points to remember.
One, don't skip the pre-implementation stages.
I've said this a couple of times on this course,
but don't skip the planning stages.
That's really going to hurt you down the road.
And be thinking about ways to justify your position.
So you need to spend a little bit of time
doing research on your own.
And then present that.
And present why you think you should do it that way.
Again, in your role,
you're going to be the expert in a lot of these types
of matters of figuring out how to architect your solution.
Second, think through key questions.
Again, be familiar with that capability matrix.
Think about how your database is going to grow,
think about the business needs for your database,
and the types of queries that's going to be run,
and where that data is going to flow out of and flow into.
So think about those things as well.
And with that, we are actually done with this lesson.
Short but sweet, but very important.
So make sure that you don't skip the pre-planning.
I'll see you in the next lesson.