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Del curso dictado por Google Cloud

Exploring and Preparing your Data with BigQuery

287 calificaciones

Google Cloud

Exploring and Preparing your Data with BigQuery

287 calificaciones

Curso 1 de 4 en SpecializationFrom Data to Insights with Google Cloud Platform

>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<< ***NEW! Specialization Completion Challenge and earn rewards valued at $100. See below for details.*** Welcome to the Coursera specialization, From Data to Insights with Google Cloud Platform brought to you by the Google Cloud team. I’m Evan Jones (a data enthusiast) and I’m going to be your guide. This first course in this specialization is Exploring and Preparing your Data with BigQuery. Here we will see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud Platform. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Cloud Dataprep to analyze and transform your datasets. This course should take about one week to complete, 5-7 total hours of work. By the end of this course, you’ll be able to query and draw insight from millions of records in our BigQuery public datasets. You’ll learn how to assess the quality of your datasets and develop an automated data cleansing pipeline that will output to BigQuery. Lastly, you’ll get to practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights. SPECIALIZATION COMPLETION CHALLENGE As if learning new skills weren't enough of an incentive, we're excited to announce a special completion challenge for 'From Data to Insights with Google Cloud Platform' specialization. Here’s how it works: Our completion challenge runs through August 31, 2018. Complete this Specialization anytime before 12:59pm PT on August 31, 2018 and we'll send you 100 Qwiklabs credits, valued at $100, that you can use to take additional labs and earn badges, which you can then add to your resume and social profiles. Your challenge awaits – begin learning on Coursera today!

De la lección

Module 1: Introduction to Data on Google Cloud Platform

Understand the core principles behind Google Cloud Platform and how to leverage them for big data analysis

Compare Big Data On-Premise vs on the Cloud12:59

Real-World Use Cases2:27

Navigate Google Cloud Platform Project Basics3:33

Conoce a los instructores

Google Cloud Training

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.

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