From Data to Insights with Google Cloud Platform

Cut Video
Course 1: Exploring and Preparing your Data
with BigQuery
Module 1: Introduction
Lesson Title: Introduction
Format: Talking head with slides
Video Name: xxx

© 2017 Google Inc. All rights reserved. Google and the Google logo are trademarks of Google Inc. All other
company and product names may be trademarks of the respective companies with which they are associated.
© 2017 Google Inc. All rights reserved. Google
and the Google logo are trademarks of Google Inc.
All other company and product names may be
trademarks of the respective companies with
which they are associated.
From Data to Insights with
Google Cloud Platform
2
v1.0

Facilities
3
Facilities Food
Parking

Course etiquette
4
Recording
this class
is prohibited
Ask questions
interactively or
via chat (online)
Please silence
your phone and
take calls outside

Agenda
5
1- Introduction to Data on the Google Cloud Platform
2 - Big Data Tools
3 - Exploring your Data with SQL in BigQuery
4 - Pricing

Agenda
6
5 - Cleaning and Transforming Data
6 - Storing and Exporting Data
7 - Ingesting New Datasets
8 - Visualization Basics with Data Studio

Agenda
7
9 - Joining and Merging Datasets
10 - Advanced Clauses and Functions
11 - Schema Design and Nested Data Structures
12 - Advanced Visualization with Google Data Studio

Agenda
8
13 - Optimizing for Performance
14 - Advanced Insights with Cloud Datalab
15 - Data Access

Audience and Prerequisites
9
Target Audiences
1. Data Analysts, Business Analysts,
Business Intelligence professionals
2. Data Engineers who will be
partnering with Data Analysts to
build scalable data solutions on
Prerequisites
1. Basic Knowledge of SQL

© 2017 Google Inc. All rights reserved. Google and the Google logo
are trademarks of Google Inc. All other company and product names may
be trademarks of the respective companies with which they are associated.
Introductions
Your instructor
• Organization
• Background
• Course goals
You
• Name
• Organization
• Job role
• Course goals
10

Pay special attention to slides with key messages or pitfalls
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Module 1
Introduction to Data on
the Google Cloud
Platform
In this module we will:
• Highlight Analytics Challenges Faced by Data
Analysts
• Compare Big Data On-Premise vs on the Cloud
• Learn from Real-World Use Cases of
Companies Transformed through Analytics on
the Cloud
• Navigate Google Cloud Platform Project Basics

Data analysts face query, infrastructure, and storage challenges
“We can only afford
to store a subset of
the data our business
generates”
“My queries are taking
way too long to run
and is stalling my
analysis.”
“We’re a data department, not
an infrastructure department.
Maintaining and upgrading
our own servers is
unsustainable.”
“My on premise
clusters aren’t scaling
with my analysis”
“We don’t have a
central data analytics
warehouse or set of
tools”
“I have no easy way to
combine and query all
the data I’ve collected”

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Module 1
the Google Cloud
Platform
Analysts
• Compare Big Data On-Premise vs
on the Cloud
the Cloud

Reasons why Google Cloud Platform
is used for Data Analysis
● Storage is Cheap
● Focus on Queries,
not Infrastructure
● Massive Scalability
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The cost of 1GB of storage has dropped dramatically
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Cost of 1 GB from 1980 to 2017 drops exponentially
Cost
per
1
GB

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1 4
3
2
Traditional big data platforms require an investment in
infrastructure

Time to Understanding
Typical Big Data
Processing
Insights
Resource
provisioning
Performance
tuning
Monitoring
Reliability
Deployment &
configuration
Handling
growing scale
Utilization
improvements

Time to Understanding
Big Data with Google:
Focus on insights,
not infrastructure.
Writing code
Insights

Source: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis non erat sem
Proprietary + Confidential
Training and Certification 20

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“[Google's] ability to build, organize, and
operate a huge network of servers and
fiber-optic cables with an efficiency and
speed that rocks physics on its heels.
This is what makes Google Google: its
physical network, its thousands of fiber
miles, and those many thousands of
servers that, in aggregate, add up to the
mother of all clouds.”
- Wired

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3
4
2
1
Edge locations
in 30+
countries
Software-
defined
networking
(why this matters)
Global data
centers
Global
network
Google Cloud Platform opens Google-scale big data analysis

You Manage
Hardware
On Premise
Your kit, someone
else’s building.
Yours to manage.
Assembly required True On-Demand Cloud
After
Storage Processing Memory Network
Ad Hoc Querying
and Scalable Storage
In the Cloud
An actual, global
elastic cloud
Invest your time in query
writing, not infrastructure
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Before

Google Cloud Platform enables on-demand scalability
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Query Processing Time
(10s, 120 Machines)
= 1 Cloud Virtual Machine
On-Premise
Underprovisioned
(demand > capacity)
(120s, 1 Machine)
Overprovisioned
(demand < capacity)

Separation of storage and computing power enables efficient
resource allocation
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Pay for ability to use processing power
even when no queries running
On-Premise Google Cloud Platform
Pay for only the resources you
are using and no more

BigQuery scales automatically and you only pay for what you use
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Fully-Managed
Infrastructure Scales
to Process Faster
.. and you only pay for
bytes processed +
storage
$

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Module 1
the Google Cloud
Platform
Analysts
the Cloud

“[Our mission is] to make our data so
intelligent it has the answer before the
question is even asked. It was a stretch
goal but essentially one that means we
have to capture all the data we produce
- both now and in the future.”
Dan Nelson - Head of Data
Ocado
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Store Petabytes of Data

“The less time that we can spend
solving problems that are already
solved, like scaling,... the more time and
energy we can spend on turning our
data into value”
Nicholas Harteau - VP Infrastructure
Spotify
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Focus on your Business, not Hardware

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Module 1
the Google Cloud
Platform
Analysts
the Cloud
• Navigate Google Cloud Platform
Project Basics

Navigate the Google Cloud Platform using the dashboard
1. Projects
2. Resources
3. Billing
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1. Projects organize and govern your activities in the cloud
● Navigate and launch cloud tools for
your project by exploring the Products
and Services menu
● Work collaboratively by adding project
users through IAM (Identity and
Access Management)
● Authorize Tools and Apps through the
API manager

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2. Resources are what you are using in the cloud
Commonly used by data analysts:
● Storage in Google Cloud Storage
○ Example: You use a Bucket for
uploading large CSV files to ingest
later for analysis
● Datasets in Google BigQuery
○ Example: You perform analysis on
raw data and create a brand new
dataset

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2. The Cloud Storage Bucket is your goto for scalable storage
● Buckets are scalable containers that
hold your data.
● You can create and upload files to your
buckets within your Cloud Console

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3. You are billed for the resources you use
After this course, try exporting BigQuery logs using this
tutorial to recreate the above Data Studio billing dashboard
Commonly used by data analysts:
● Storage in Google Cloud Storage
○ Billed for Bucket Storage
● Datasets in Google BigQuery
○ Billed for Query processing
○ Billed for Table Storage

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Manage and
monitor your
project resources
in one place
Efficiently scale
your compute
and storage
needs
Overcome query
speed,
infrastructure,
and cost
challenges
Summary: GCP offers you the ability to:
Module Summary: Scale with the Google Cloud Platform
Evangelize data
analysis in your
organization

Lab 0
Getting Started with
Google Cloud Platform and Qwiklabs
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Getting started with Google Cloud Platform and Qwiklabs
● Open an incognito window
● Navigate to:
googlecloud.qwiklabs.com
● Create a new account with the
email address you used when
you registered for this course

What you get
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For each lab, Qwiklabs offers:
• A free set of resources for a fixed
amount of time
• A clean environment with
permissions

Qwiklabs sign-in process
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Open an incognito browser
From the incognito browser,
sign in to Qwiklabs
Select the lab and
click Start Lab
Sign in to the GCP
console using the
provided credentials

Open Qwiklabs
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Open an incognito window
(or private/anonymous window).
Go to the Qwiklabs URL your
instructor provides.
Username
Password
Sign in and launch the course
(with credentials you used to register
for the course).
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2
3

View your labs
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Lab completed
Active lab
Not yet available

Select a lab
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You cannot pause and restart

1. Click
2. Note the following
3. Click and sign in
4. Accept terms and note the project
Run a lab

End a lab
45
Some labs may require you to NOT end the lab; the instructor will
inform you.
● When done, click to free your resources.

Course materials: End of class
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Click Materials on
the top navigation bar.
Select the class from the
Course Materials list.
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1
From Data to Insights on Google Cloud Platform

From Data to Insights with Google Cloud Platform

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