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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 is at the project level.
So you could have many different users for your projects
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 you create is all under this project name.
So, we have projects,
you have resources that you're going to consume
and then the building that you can be charge for these 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 data prep,
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 eventually, be building up tens or]f
flow machinery 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 using Cloud Storage buckets and BigQuery datasets,
as for data analysts.
So Cloud Storage buckets, that's expandable container.
Its best way it's been taught to me,
is its a staging area.
They can 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 a 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 do you 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
the second course of the specialization in that dashboard.
But again, you're build 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 are 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, the following course modules,
we'll introduced the actual specific tools, like BigQuery,
Data Studio and Cloud Data Prep,
and how they build on the compute and storage scalability of the Google Cloud Platform.
Google Cloud Training
