Okay. As promised, I want to keep this zippy. So, we're going to look at the Google Cloud platform and a few screenshots here, and we're going to set you up with a sandbox account as part of your lab zero. So here's the dashboard, as you're going to see in just a second. Three things that I want to call your attention to. One, everything in the Google Cloud platform, the umbrella resource that you're going to be using is called the project. So everything's at the project level. So you could have many different users for your project and you can have many different resources that you're using as part of your project as well. So any type of access or BigQuery dataset that you create is all under this project name. So you have projects, you have resources that you're going to consume and then the billing that you can be charged for those resources. So let's dive into each of those really quickly. So, projects organize all of your activities. So again, since this course is really focused on big data, this is largely going to be your BigQuery, your Cloud Dataprep, your Google Cloud storage buckets, but this doesn't limit yourself there. If you want to go crazy and take this course and every other course that our Google Cloud team has created, you'll be using a lot more than just those few technologies. You'll eventually be building up TensorFlow machine learning models and dealing with API authorization and dealing with apps, and that's all in the same dashboard interface, right? So once you learn this once, then you're good and you just keep plugging in more cool tools and technologies into it. And of course, it's collaborative. All right. So let's talk about some of those tools, right? So here's the tools in your toolkit. Two large ones that you can use as part of the specialization, you can be using Cloud Storage buckets and BigQuery datasets, as for data analysts. So Cloud storage buckets, it's an expandable container, the best way that its been taught to me is it's a staging area - that you can just throw a ton of your data, CSV files, JSON files, whatever you want - get all that good stuff stored in Google Cloud Storage, and then you can ingest that into BigQuery. Ingest that into BigQuery in the form of datasets. And one of the great things about Google BigQuery is data loves data. So, you can get really meta with BigQuery and you can see how much your organization is actually using BigQuery and how many folks are running successful queries, failed queries, how much data that you're actually scanning and processing, and you can visualize all that in this particular case is a Google Data Studio dashboard, and we'll cover how to create and visualize your insights in this second course of this specialization in that dashboard. But again, you're billed for those resources that you use, and you can monitor those actual resources that you're using, and we'll cover the pricing of BigQuery, and how much you're going to be charged for processing those bytes of data in later modules as well, and how you can cost optimize and potentially set up those custom quotas if you worry about other users in your organization blowing your budget. Wrapping up this module, let's review some of the key points about Google Cloud Platform. We've covered some of the common challenges data analysts face and how the Cloud offers scalable, fully-managed tools for any data analyst to use. Now in the following course modules, we'll introduce the actual specific tools, like BigQuery, Data Studio and Cloud DataPrep, and how they build on the compute and storage scalability of the Google Cloud platform.
