Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools

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Exploring and Preparing your Data with BigQuery

735 classificações

Google Cloud

Exploring and Preparing your Data with BigQuery

735 classificações

Curso 1 de 4 em Specialization From Data to Insights with Google Cloud Platform

***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $120! 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 wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'From Data to Insights on Google Cloud Platform’ specialization. Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $120 value given there are 4 courses in the specialization). You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles. >>> 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 <<<

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Module 2: Big Data Tools Overview

Learn what are the key big data tools on Google Cloud Platform that you will be using to analyze, prepare, and visualize data

Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools5:01

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In this next module, we will highlight the five common tasks of any data analyst and

map those to their respective tools on Google Cloud Platform.

After that, we'll head straight into a demo showing BigQuery operating on over

a billion records.

Following the demo, we'll explore the BigQuery feature set itself and end with

a discussion comparing data analysts, data scientists, and data engineers.

Okay, so before we get into the really cool part,

which is showing you the useful big data tools on Google Cloud Platform,

we first have to talk about the data analyst tasks themselves as a whole.

So here are the five things that any data analyst worth their salt is going to

perform.

You're going to ingest data, you're going to transform it, clean it up.

All data is dirty data.

And then you're going to be creating some reporting data tables and

storing that data for analysis, which is that fourth step.

Finally, look how far we've [LAUGH] come.

It took four steps to actually take to get to the analysis portion where you're

writing these cool, sophisticated queries to get insights from your data.

And then you're pairing that, potentially, with a visualization tool or

platform to really make those insights shine and explain them to people.

But the road is fraught with challenges.

So at each of these different steps, as we saw with some of the challenges

that organizations face, or data analysts have faced earlier on,

each of these different steps has their own pitfalls.

So ingestion, you've got petabytes of data, it's going to bottleneck your tool.

You don't even begin to imagine loading all of your data at once.

So unfortunately, you're loading only in a sample, or

you're looking only at a small amount of your data.

So you can't really make amazing progress with loading

all of your data in at once or it just takes forever.

Second, transforming the data.

It's slow going.

Perhaps you have to either rely on another team, a data engineering team,

to write sophisticated pipelines to transform your data.

And you wish there was an easier way to either write it yourself, or

some kind of cool tool that'll help you build these things up

in just a little bit of an easier way.

And that was a clear spoiler alert for

one of the tools you're going to be learning on the next slide.

[LAUGH] So onto storage.

Scaling up the amount of data that you need to store, as we mentioned before,

has been a problem for

organizations that have managed their own hardware internally or relied on things

that aren't as inherently scalable as relying on Google Cloud Platform analysis.

Your queries are bottlenecking, your data is in many different places and

there's no easy way to mash it together.

Visualizing your insights, you have amazing insights that you want to show.

And as soon as you go to present it to your stakeholders and your peers,

your tool starts to lag.

You want to filter down and drill down into a particular insight and

you have a 30-minute meeting.

And, unfortunately, it takes the tool ten minutes to load and

drill down into that insight.

And then it's, you've lost the audience's attention by that point as well.

Let's see where the Google Cloud Platform can step in.

So here's the right tools for scalability, and

this will allow you to address and overcome a lot of these challenges.

So ingestion, Google Cloud Platform, BigQuery in particular,

is a petabyte-scale data analytics platform.

And one of the great things that we're going to cover in the ingestion part, or

the pricing lab that you're going to do,

is actually importing data into BigQuery in batch form is free, which is great.

Transforming your data, so say you wanted to write some simple SQL.

You can just do that directly inside of BigQuery.

Or if you didn't even want to write any SQL, and

one of the cool labs we're going to do later on

is using a tool called Cloud Data Prep where you can chain together, through

a graphical user interface, a neat visual flow of how you want to process the data.

So say you wanted to drag and drop a deduplication and

then parse this particular field.

You can do that visually,

and you'll get a lot of practice with that as part of this course.

Storing data, again, we mentioned it a lot, Google Cloud Storage, inexpensive.

BigQuery itself, you're going to see in the pricing lab,

it's as of the time of this recording, is $0.02 per gigabyte per month.

If the data is there for a long time, that storage cost is cut in half.

Analysis, that's really where BigQuery shines.

I'm going to really go into the nine core parts of its feature set shortly.

And this is managing scale, right, fully managed.

No dev ops, managing it without you managing your servers,

just write cool SQL.

Last but not least, visualization tools.

Google has built Google Data Studio,

which is one of the free visualization tools that can sit on top of BigQuery.

And then you let all the BigQuery processing do all the hard, heavy lifting.

And then rely on a tool like Google Data Studio or

Tableau or Looker or QlikView to do that visualization for you as well.

So each tool for a different use case.

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