Join Lynn Langit for an in-depth discussion in this video Introducing Amazon Web Services, part of Amazon Web Services: Data Services.
- We're gonna start by talking about Amazon Web Services in general. They have been in the commercial market for the longest of any commercial vendor and their products are the most mature because of that. Their products are also priced at a very good value. They often lead the market in terms of the best pricing, or the cheapest pricing, for their particular services. And in fact, price cuts are common in this industry and they're usually lead by Amazon and then followed by other cloud providers. As a working big data and cloud architect, I most often use Amazon Web Services in my production workloads.
Amazon has a frequent product release cycle and update cycle. It's not uncommon to have more than 10 updates or releases in the period of one week's time. A tip that I have right from the beginning of this course is to subscribe to the Amazon Web Services' main blog so that you can get notifications when their products release or update. Another consideration as we're working with Amazon Web Services is to understand where their physical data center locations are.
They're shown here in two aspects. The first is their primary centers and then the second is the zones within their centers. So I'm recording this course from North america. If you are working with some of the aspects of the course, be sure to select the appropriate region and zone from where you are watching the course. As we drill in to Amazon Services, let's think about the categories those services are grouped into. As a cloud architect, I categorize cloud based services as compute, which is now virtual machines, docker containers and in the case of Amazon, something called a Lambda which is a service that has some applicability to the architecture's and services we're going to talk about in this course.
The core of this course, though is not compute, it's storage. We're gonna be talking about Amazon Web Services around file storage and databases. And there are so many choices and so much information, this is the core of this entire course. There are, of course, other services that are available through the Amazon set of services in addition to compute and storage and they're very often used in production workloads, as well. So for most of this course, we'll be working with the Amazon services as they're available through the Amazon Web Services console, which is a website.
So what I've done here is I've set up a new account for demonstration and logged in. The way it works with Amazon is you have access to try out some, but not all, of the services that we're gonna show for free. So I'll try to point out as we're going through learning how the services work and how you might test them, which are available for free tier and which aren't. But I'm going to give you a tip when you are setting this up if you click on your name and then you click on Billing & Cost Management what you can do is you can set up a budget.
Now, what I've done is I've set up a budget associated to any resources that have a tag that is called Lynda associated to this. So if you click Create Budget, you put in the name, and then you include costs related to, and I created a Tag. And I'll show you how to do that in just a minute. And then I set a time period and a monthly amount and then I set a billing alarm. This is a real world tip that when you're trying out services you'll really want to do because in case you forget to turn them off, you don't want to be billed accidentally. So let me show you how to create a tag.
So you create a group and I'll call this one demo and then I'll put a key called Name and I'll call it demo and then I'll just save it. And so now if I click, I have two resource groups. I had created this Lynda one in advance. If I want to edit it then I can go into my Tag Editor and then I can look for a tag key and I can look for a value and edit it. So it's a little bit of setup, but this is a tip from the real world because I don't want you to get charges that you didn't intend when you're trying out some of these services.
- Why cloud tools matter
- Storage choices on AWS
- RDBMSs such as Core RDS, Aurora, and Oracle
- Working with semistructured data in NoSQL
- Connecting to data warehouses such as AWS Redshift and Snowflake
- Graph databases and AWS Machine Learning
- Working with Hadoop
- Common data scenarios and architectures