Hello, everybody. And welcome to this lecture. And it's like, Sure, we're gonna be covering the introduction to eight of US data basis
and let's go ahead and dive right into it.
So databases and Amazon Cloud start with R. D s R DEA stands for relational database service.
It basically is going to handle SQL Server Oracle, my SQL Post grad ask you, Aurora and Maria de be Now many of you probably recognize a lot of these database types, one that stands out, that you may not recognize his Aurora
Aurora's actually AWS is baby product. When it comes to already. Yes, that is their proprietary alternative to some of the other more popular names, and they have their own separate pricing model. You don't need to know a whole lot about Aurora aside from the fact that it is it of, yes, this stuff.
But if you are interested, I will make sure to attach an Aurora
uh, f A Q link so you can read a little bit more information about their product. But this is basically what RGs is as a whole. So if you have any experience with relational databases, this, um, Artie s AWS already s is actually what the service would be called and that it's a service that handles
that relation. All databases in the cloud. So what is a relational database? Well, if you're not familiar with data basing, the best example that I could give you is a spreadsheet. Ah, if you're familiar with Microsoft Excel or Google Sheets, chances are you've created tables or at least seeing
tables, any of altered rose and looked at, you know, the columns and the data that's within it.
That's basically what a relational database is is basically just a large table with
lots of data within it within the rose on the columns that help you organize,
um, whatever type of data that you're trying to keep track of it could be for business reasons. It could be for personal reasons. It could be for personal finance or what have you. It's just the database type.
And when we're talking about relational databases, you know it goes from personal finance to, you know, where you're calculating dollars and subtracting dollars to actually holding. You know, the information behind a website or an application,
and they can grow to very massive size is We've been using relational databases for decades now, and, uh, as if lates, we've actually advanced those relational databases to the cloud. So there are two things that I want to talk about when it comes to Eight of us are D s, and that is multi ese and re replicas.
multi ese is actually the built in fail over backup system for the RGs databases. So
for those of you who are not familiar with data basing, imagine you have a website. It's a very big website. Let's say it's like Amazon and you have a ton of products and the products are the product information, the pricing, the shipping location from which warehouse they're gonna be shipped from so they can be sent over to your doorstep. All that stuff all these,
uh, intricacies with the transaction
are held within these many, many databases. And let's say
all of a sudden one of the databases fail.
multi easy, basically is going to say, OK,
one database just fell. We're gonna go ahead and switch over to the other database and another availability zone that is not failed. It's still running, We're good and we're going to stay on that one until the original one spends back up
essentially what multi ese does. It just keeps the data flowing. It keeps the application moving. That way. Your business doesn't lose money,
things. They're still available. You can still continue operating in your business.
Um, but that's what multi ese is. It basically just keeps things flowing, making sure that your organization isn't losing money and that the application is still able to access the data necessary in order to keep clients and customers happy.
Read replicas Air a little bit different from multi ese. They actually don't deal with data fail over like multi ese. Instead,
what they're going for is performance. So
you know you have your mean database and let's say you had an influx of traffic on your website. All of a sudden, that database is just getting hammered because everybody's on the website. They are just, you know, checking on all the products. Let's say it's like Black Friday or something like that.
That database is likely just gonna act super sluggish. It's just not gonna be able Thio send the data fast enough, and what happens is,
you know, the clients are gonna end up bleeding the website because it's just not working. They're gonna get frustrated. They're going to go somewhere else. And ultimately your organization loses money.
Well, we don't want that. So
where you could do is you can assign a read replica
and basically share that traffic so that not everybody's being
sent to the master database. In that master databases and being hammered instead
The requirements on the databases are now being shared across two or three or multiple, and it's able to offer the amount of information necessary, and profit margins are still running.
The website, still running everything is, is doing much better, so it's is aiming for performance. It's optimizing the data performance by sharing the request, and there is no automatic fail over. But like I said, we're not really working. We're not really worried about fail over here. We're really worried about performance.
And so that's what Reed Replica
is going to dio for the certified cloud practitioner. You basically just need to understand the difference between multi ese and read replicas. Make sure you understand the difference is you probably will be asked a question or two on these these different components within our T s.
Make sure you d'oh memorize these two features of our DNC. There
is a good chance you will be asked questions on it in your exam.
So moving on, I want to talk about something that is not relation a land that is a
non relation all database. Uh, AWS the service is Dynamo Devi, actually, that that is their service that is their non relation all database service. And basically, it's another form of data basing kind of like R d s. It does have, you know, tables and cells and things like that.
However, it's a lot more scalable
that's operate differently. When you're adding information into a ah dynamodb or a non relation A ll database, you're actually adding in the form of Jason, which is actually beyond the scope of this exam. When you're moving on to the associate level exams, you will need to understand
how dynamo TV works a little bit more,
but a ce faras the CCP exam. Really. The only thing you need to understand is that dynamodb is the non relation all database service. If you are interested in learning more, I encourage you to go to AWS is Web site and read up on the service. The next component that I want to talk about for interview s data basing his Amazon red shift.
red shift contains two different components. You have only transaction processing, which is O L T P. And then you have online analytics processing, which is all a p. They are different.
Let's start off with online transactions processing first. So basically, the best way to think of oil T p
when you go to a large e commerce site like eBay or Amazon,
you're gonna enter in your information. And all of a sudden everything is there like you enter your password and your user name, and all of a sudden you're all your purchases, said you created your accounts are available to you so you can go on Dhe. Find out what you bought two years ago.
Ah, your credit card information is stored. Your addresses, they're all these. All these things were there for you.
And they're not all stored on one on one server. There's no way that's possible. First large e commerce site
to, you know, be able to service people across the world. You have to have many, many servers, probably hundreds of thousands of servers in order to handle. Ah, you know, customers of that scale. So
what data warehousing is gonna be doing is basically
taking the information from all these different servers from all these different places that are around the world
and aggregating it into one place, so that when you log in to your
portal when you log into your your eBay account or your Amazon account,
all your information's there.
It's not all stored on one server, but it is all being aggregated into one place for you. And that's kind of what well TP focuses on. It basically says, All right,
this user just logged in. Let's go ahead and get everything that we need and put it into the portal so that they can access it.
Online Analytics processing is a little bit different.
Basically, what it's gonna be doing is performing intense queries across multiple databases to provide very valuable information to the right people so they can make the right decisions.
A good example of this is, you know, every year around the holidays and United States, we have a beverage that's called the Pumpkin Spice lattes that you know, Starbucks and other popular coffee shops. They offer that
well, if these coffee shops, we're gonna be selling the product every year. One thing that the large conglomerates like Starbucks and Dunkin Donuts and other coffee shops they're gonna have to do is they're going to have to analyse which coffee shops last year ended up selling out which ones you know, we're not able to deliver on demands,
and they want to make sure that they are delivering
the right amount of products to those shops this year. Biggest chances are if last year there was such a demand, maybe this year there'll be even more for demands, or at least the same amount of demands.
That's something that online analytics process and can do is that it's going to just basically
calculate the pros and cons. It's gonna be calculating
all these different intricacies when it comes to the data
in order to make a profitable decision, and and ultimately this
benefits the company and it'll benefit Starbucks, and it'll benefit other coffee shops because
they're able to provide the supply for the demands and exceed that. If necessary,
so is usually used by management to perform these advanced queries. And I would encourage you to take a look
at the Amazon red shift. F ake us learn a little bit more about the service's. But as faras thes certified cloud practitioner, I would make sure that you understand what oh L t p stands for what it is and what Oh, l A P is as well.
And the last service that I want to talk about for Amazon rd s and
data basing is the last to cash. So, alas, the cash is a Web service that's gonna perform in memory cache in the cloud.
It's basically a service that provides performance for your web baths by cashing the frequently queried items and data. So what does that mean?
Let's say you're going to Amazon. We're gonna go back to that example. Let's say you're going to have a song and
you're checking the products. You know, there's the hot items, right? You go to the home page, you see frequently search for items,
and what you're seeing, actually, is
of users in the area who searched for
these items and what they're doing is they're saying, Hey, 100 people in your city. We're searching for umbrellas,
so we're going to send this to us. Well, because chances are you might be interested in an umbrella.
this way we can optimize our server resource is because our resource is aren't being constantly pulled from thes air things that are constant search for based on your location.
And also we have a better chance of making a sale on a profit because
maybe it's raining a lot where you're located. So that's basically what a last the cash is doing. A last. The cash is basically going to provide the information that is frequently queried frequently access by you or by people in your region. And that way they can
save on the resource is of the servers and the two
offerings the to open source cash engines that, alas, the cashews. This is ma'am, cash, T and readiness. Basically, these are two things that you want to remember going in. Remember last. The cash is part of the data basing service, and the two open source cashing engines will be ma'am, cash, tea and read this.
are likely to be on the exam. All right, everybody, that about finishes of this lecture? If you have any questions, please feel free to reach out to me. And if not, then I will see you in the next lecture.