Cloud computing overview & Technical intro to Google Cloud

Cloud computing overview &
Google Cloud technical intro
Wesley Chun
Developer Advocate, Google
G Suite Dev Show
goo.gl/JpBQ40
About the speaker
Developer Advocate, Google Cloud
● Mission: enable current and future
developers everywhere to be
successful using Google Cloud and
other Google developer tools & APIs
● Videos: host of the G Suite Dev Show
on YouTube
● Blogs: developers.googleblog.com &
gsuite-developers.googleblog.com
● Twitters: @wescpy, @GoogleDevs,
@GSuiteDevs
Previous experience / background
● Software engineer & architect for 20+ years
● One of the original Yahoo!Mail engineers
● Author of bestselling "Core Python" books
(corepython.com)
● Technical trainer, teacher, instructor since
1983 (Computer Science, C, Linux, Python)
● Fellow of the Python Software Foundation
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)

Why and Agenda
● Cloud has taken industry by storm (all?)
● Not enough developer awareness of cloud computing
● Need to prep next-generation cloud-ready workforce
● Challenging to keep up with latest industry trends
1
Cloud computing
overview
2
Google Cloud
(GCP + G Suite)
3
Serverless
platforms
4
Inspirational
Ideas
5
Summary &
wrap-up
Cloud computing overview
All you need to know about the cloud1

What is cloud computing?
spar
Google Compute Engine, Google Cloud Storage
AWS EC2 & S3; Rackspace; Joyent
Cloud service levels/"pillars"
SaaS
Software as a Service
PaaS
Platform as a Service
IaaS
Infrastructure as a Service
Google BigQuery, Cloud SQL,
Cloud Datastore, NL, Vision, Pub/Sub
AWS Kinesis, RDS; Windows Azure SQL, Docker
Google Apps Script, App Maker
Salesforce1/force.com
G Suite (Google Apps)
Yahoo!Mail, Hotmail, Salesforce, Netsuite
Google App Engine, Cloud Functions
Heroku, Cloud Foundry, Engine Yard, AWS Lambda

Outsourcing of apps (SaaS)
SaaS
PaaS
IaaS
Outsourcing of hardware (IaaS)
SaaS
PaaS
IaaS

Outsourcing of logic-hosting (PaaS)
SaaS
PaaS
IaaS
IaaS/PaaS gray area (DataB/S/P-aaS?)
SaaS
PaaS
IaaS

SaaS/PaaS gray area
SaaS
PaaS
IaaS
Summary of responsibility
SaaS
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
IaaS
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
PaaS
Managed by YOU Managed by cloud vendor
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
on-prem
all you, no cloud

2 Introduction to
Google Cloud
GCP and G Suite

G Suite APIs
Top-level documentation and comprehensive developers
overview video at developers.google.com/gsuite

SaaS
PaaS
IaaS
Google Cloud Platform vs. G Suite
G Suite
APIs
GCP
APIs
Compute
(running code)

Running Code: Compute Engine
>
Google Compute Engine
cloud
Running Code: App Engine
Google App Engine
we
>
cloud

Running Code: Cloud Functions
Google Cloud Functions
cloud
firebase
Running Code: Cloud Run
Google Cloud Run
cloud

Storage
(where to put your data)
Storing Data: Cloud Storage & Cloud Filestore
cloud
cloud

Storing Data: Cloud SQL
cloud
Storing Data: Cloud Datastore
Cloud Datastore
NoSQL
cloud

Storing Data: Firebase
Firebase
JSON
real-time
firebase
Storing Data: Cloud Firestore
Cloud
Datastore
Firebase
cloud

Machine Learning
(analyze your data)
Machine Learning… where are you?
● Aware: read up on ML its ability to help derive
insights based on a massive amount of data
● User: awareness, but putting ML to use by
using existing tools or calling APIs; requires
ZERO prior knowledge of ML
● Scientist: know enough about ML, have the
technical skills to build, train, and deploy
predictive models & make new predictions

Full Spectrum of AI & ML Offerings
App Developer Data Scientist ML Scientist/Researcher
Use pre-built models Use/extend OSS SDK
ML EngineAuto ML
Build custom models
ML APIs
Storing and Analyzing Data: BigQuery
Google BigQuery
cloud

Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
cloud
Machine Learning: Cloud Vision & Video Intelligence
Google Cloud Vision & Video
Intelligence APIs
cloud
cloud

Machine Learning: Cloud Speech
Google Cloud Speech APIs
cloud
cloud
Machine Learning: AutoML
AutoML:
cloud
cloud

Machine Learning: Cloud ML Engine
Google Cloud Machine Learning Engine
cloud
Data transformation/ETL
● Open sourced as Apache
Beam (supports Apache
Spark and Flink)

G Suite
(collaborate & communicate)
G Suite: Gmail
Gmail API
read &
send messages labels
search manage settings
developers

G Suite: Google Calendar
Calendar API
access modify create
events
developers
G Suite: Google Docs & Slides
Docs & Slides APIs
developers
developers

G Suite: Google Sheets
Sheets API
developers
G Suite: Google Drive
Drive API read
write permissions/sharing
import/export
developers

REST API examples
Short Python code snippets using GCP & G Suite APIs
API key (public data) vs. OAuth2 access (private data)

BUT
... wait, there’s more...

The first word on Security
Authentication ("authn") vs authorization ("authz")
● authn: you are who you say you are
○ login & passwd
○ handprint authentication
○ retina scan
● authz: okay, you are who you say you are, but can you haz data?
○ OAuth2 - mostly authz, but some authn
○ Mostly about 3rd-party access to data
○ Users must give YOUR code access to THEIR data
○ Most of the time when you see "auth", it refers to authz
● Some refer to this as "consent" vs. "credentials…" which is which?
Cloud/GCP console
console.cloud.google.com
● Hub of all developer activity
● Applications == projects
○ New project for new apps
○ Projects have a billing acct
● Manage billing accounts
○ Financial instrument required
○ Personal or corporate credit cards,
Free Trial, and education grants
● Access GCP product settings
● Manage users & security
● Manage APIs in devconsole

● View application statistics
● En-/disable Google APIs
● Obtain application credentials
Using Google APIs
goo.gl/RbyTFD
API manager aka Developers Console (devconsole)
console.developers.google.com
OAuth2 or
API key
HTTP-based REST APIs 1
HTTP
2
Google APIs request-response workflow
● Application makes request
● Request received by service
● Process data, return response
● Results sent to application
(typical client-server model)

Google APIs client
libraries for many
languages; demos in
developers.google.com/
api-client-library
SIMPLE
AUTHORIZED
Which do you choose?

List (first 100) files/folders in Google Drive
from __future__ import print_function
from googleapiclient import discovery
from httplib2 import Http
from oauth2client import file, client, tools
SCOPES = 'https://www.googleapis.com/auth/drive.metadata.readonly'
store = file.Storage('storage.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets('client_secret.json', SCOPES)
creds = tools.run_flow(flow, store)
DRIVE = discovery.build('drive', 'v3', http=creds.authorize(Http()))
files = DRIVE.files().list().execute().get('files', [])
for f in files:
print(f['name'], f['mimeType'])
Listing your ﬁles
goo.gl/ZIgf8k
Try our Node.js customized reporting tool codelab:
g.co/codelabs/sheets
Why use the Sheets API?
data visualization
customized reports
Sheets as a data source

Migrate SQL data to a Sheet
# read SQL data then create new spreadsheet & add rows into it
FIELDS = ('ID', 'Customer Name', 'Product Code',
'Units Ordered', 'Unit Price', 'Status')
cxn = sqlite3.connect('db.sqlite')
cur = cxn.cursor()
rows = cur.execute('SELECT * FROM orders').fetchall()
cxn.close()
rows.insert(0, FIELDS)
DATA = {'properties': {'title': 'Customer orders'}}
SHEET_ID = SHEETS.spreadsheets().create(body=DATA,
fields='spreadsheetId').execute().get('spreadsheetId')
SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID, range='A1',
body={'values': rows}, valueInputOption='RAW').execute()
Migrate SQL data
to Sheets
goo.gl/N1RPwC
Try our Node.js BigQuery GitHub license analyzer codelab:
g.co/codelabs/slides
Why use the Slides API?
data visualization
presentable reports

Try our Node.js Markdown-to-Google-Slides generator:
github.com/gsuitedevs/md2googleslides
Why use the Slides API?
customized presentations
Replace text & images from template deck
requests = [
# (global) search-and-replace text
{'replaceAllText': {
'findText': '{{TITLE}}',
'replaceText': 'Hello World!',
}},
# replace text-based image placeholders (global)
{'replaceAllShapesWithImage': {
'imageUrl': IMG_URL, # link to product logo
'replaceMethod': 'CENTER_INSIDE',
'containsText': {'text': '{{LOGO}}'},
}},
]
SLIDES.presentations().batchUpdate(body={'requests': requests},
presentationId=DECK_ID, fields='').execute()
Replacing text
and images
goo.gl/o6EFwk

+
Mail merge
=
Mail merge (template search & replace)
requests = [
# (global) search-and-replace text
{'replaceAllText': {
'containsText': {'text': '{{TITLE}}'},
'replaceText': 'Hello World!',
}},
]
DOCS.documents().batchUpdate(body={'requests': requests},
documentId=DOC_ID, fields='').execute()
Mail merge
goo.gle/2KrPNeG

BigQuery: querying Shakespeare words
TITLE = "The top 10 most common words in all of Shakespeare's works"
QUERY = '''
SELECT LOWER(word) AS word, sum(word_count) AS count
FROM [bigquery-public-data:samples.shakespeare]
GROUP BY word ORDER BY count DESC LIMIT 10
'''
rsp = BQ.query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
for col in rsp['schema']['fields']: # HEADERS
print(col['name'].upper(), end='t')
print()
for row in rsp['rows']: # DATA
for col in row['f']:
print(col['v'], end='t')
print()
Top 10 most common Shakespeare words
$ python bq_shake.py
*** Results for "The most common words in all of Shakespeare's works":
WORD COUNT
the 29801
and 27529
i 21029
to 20957
of 18514
a 15370
you 14010
my 12936
in 11722
that 11519

Simple sentiment & classification analysis
TEXT = '''Google, headquartered in Mountain View, unveiled the new
Android phone at the Consumer Electronics Show. Sundar Pichai said
in his keynote that users love their new Android phones.'''
print('TEXT:', TEXT)
data = {'type': 'PLAIN_TEXT', 'content': TEXT}
NL = discovery.build('language', 'v1', developerKey=API_KEY)
# sentiment analysis
sent = NL.documents().analyzeSentiment(
body={'document': data}).execute().get('documentSentiment')
print('nSENTIMENT: score (%s), magnitude (%s)' % (sent['score'], sent['magnitude']))
# content classification
print('nCATEGORIES:')
cats = NL.documents().classifyText(body={'document': data}).execute().get('categories')
for cat in cats:
print('* %s (%s)' % (cat['name'][1:], cat['confidence']))
Simple sentiment & classification analysis
$ python nl_sent_simple.py
TEXT: Google, headquartered in Mountain View, unveiled the new Android
phone at the Consumer Electronics Show. Sundar Pichai said in
his keynote that users love their new Android phones.
SENTIMENT: score (0.3), magnitude (0.6)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)

Text-to-Speech: synthsizing audio text
# request body (with text body using 16-bit linear PCM audio encoding)
body = {
'input': {'text': text},
'voice': {
'languageCode': 'en-US',
'ssmlGender': 'FEMALE',
},
'audioConfig': {'audioEncoding': 'LINEAR16'},
}
# call Text-to-Speech API to synthesize text (write to text.wav file)
T2S = discovery.build('texttospeech', 'v1', developerKey=API_KEY)
audio = T2S.text().synthesize(body=body).execute().get('audioContent')
with open('text.wav', 'wb') as f:
f.write(base64.b64decode(audio))
Speech-to-Text: transcribing audio text
# request body (16-bit linear PCM audio content, i.e., from text.wav)
body = {
'audio': {'content': audio},
'config': {
'languageCode': 'en-US',
'encoding': 'LINEAR16',
},
}
# call Speech-to-Text API to recognize text
S2T = discovery.build('speech', 'v1', developerKey=API_KEY)
rsp = S2T.speech().recognize(
body=body).execute().get('results')[0]['alternatives'][0]
print('** %.2f%% confident of this transcript:n%r' % (
rsp['confidence']*100., rsp['transcript']))

Speech-to-Text: transcribing audio text
$ python s2t_demo.py
** 92.03% confident of this transcript:
'Google headquarters in Mountain View unveiled the new
Android phone at the Consumer Electronics Show Sundar
pichai said in his keynote that users love their new
Android phones'
Video intelligence: make videos searchable
# request body (single payload, base64 binary video)
body = {
"inputContent": video,
"features": ['LABEL_DETECTION', 'SPEECH_TRANSCRIPTION'],
"videoContext": {"speechTranscriptionConfig": {"languageCode": 'en-US'}},
}
# perform video shot analysis followed by speech analysis
VINTEL = discovery.build('videointelligence', 'v1', developerKey=API_KEY)
resource = VINTEL.videos().annotate(body=body).execute().get('name')
while True:
results = VINTEL.operations().get(name=resource).execute()
if results.get('done'):
break
time.sleep(random.randrange(8)) # expo-backoff probably better

# display shot labels followed by speech transcription
for labels in results['response']['annotationResults']:
if 'shotLabelAnnotations' in labels:
print('n** Video shot analysis labeling')
for shot in labels['shotLabelAnnotations']:
seg = shot['segments'][0]
print(' - %s (%.2f%%)' % (
shot['entity']['description'], seg['confidence']*100.))
if 'speechTranscriptions' in labels:
print('** Speech transcription')
speech = labels['speechTranscriptions'][0]['alternatives'][0]
print(' - %r (%.2f%%)' % (
speech['transcript'], speech['confidence']*100.))
$ python3 vid_demo.py you-need-a-hug.mp4
** Video shot analysis labeling
- vacation (30.62%)
- fun (61.53%)
- interaction (38.93%)
- summer (57.10%)
** Speech transcription
- 'you need a hug come here' (79.27%)

Higher-level GCP SDK & API client libraries
1. Bad news: Just showed you the "harder
way" of using Google Cloud Platform APIs
2. Good news: it's even easier with the GCP
SDK and higher-level client libraries
3. Why (not)? Not all Google APIs have high-
level client libraries. Lower-level serves as
"LCD" for accessing more Google APIs
cloud.google.com/sdk
cloud.google.com/apis/docs
3
Run your code on Google
Cloud serverless
GCP: Google App Engine , Google Cloud Functions
G Suite: Google Apps Script , Google App Maker

Serverless: what & why
● What is serverless?
○ Misnomer
○ "No worries"
○ Developers focus on writing code & solving business problems*
● Why serverless?
○ Fastest growing segment of cloud... per analyst research*:
■ $1.9B (2016) and $4.25B (2018) ⇒ $7.7B (2021) and $14.93B (2023)
○ What if you go viral? Autoscaling: your new best friend
○ What if you don't? Code not running? You're not paying.
* in USD; source:Forbes (May 2018), MarketsandMarkets™ & CB Insights (Aug 2018)
SaaS
PaaS
IaaS
Serverless: PaaS-y compute/processing

Google App Engine
App-hosting in the cloud
Why does App Engine exist?
● Focus on app not DevOps
○ Web app
○ Mobile backend
○ Cloud service
● Enhance productivity
● Deploy globally
● Fully-managed
● Auto-scaling
● Pay-per-use
● Familiar languages

Hello World (3 files: Python "MVP")
app.yaml
runtime: python37
main.py
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello World!'
requirements.txt
Flask==1.0.2
Deploy:
$ gcloud app deploy
Access globally:
https://PROJECT_ID.appspot.com
Open source repo at
github.com/GoogleCloudPlatform/python-docs-samples/
tree/master/appengine/standard_python37/hello_world
Google Cloud Functions
Function-hosting in the cloud

Why does Cloud Functions exist?
● Don't have entire app?
○ No framework "overhead" (LAMP, MEAN...)
○ Deploy microservices
● Event-driven
○ Triggered via HTTP or background events
○ Auto-scaling & highly-available
○ Pay per use
● Familiar development environment
○ Cmd-line or developer console
● Cloud Functions for Firebase
○ Mobile app use-cases
● Available runtimes
○ JS/Node.js 6, 8, 10
○ Python 3.7
○ Go 1.11, 1.12
○ Java 8
main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python37 --trigger-http
Access globally (curl):
curl -X POST https://GCP_REGION-PROJECT_ID.cloudfunctions.net/hello
-H "Content-Type:application/json"
Access globally (browser):
GCP_REGION-PROJECT_ID.cloudfunctions.net/hello
Hello World (Python "MVP")

Google Apps Script
Customized JS runtime for automation, extension, and integration
with G Suite and other Google or external services
What can you do with this?

Accessing maps from
spreadsheets?!?
goo.gl/oAzBN9
This… with help from Google Maps & Gmail
function sendMap() {
var sheet = SpreadsheetApp.getActiveSheet();
var address = sheet.getRange("A2").getValue();
var map = Maps.newStaticMap().addMarker(address);
GmailApp.sendEmail('friend@example.com', 'Map',
'See below.', {attachments:[map]});
}
JS
Google Cloud Run
Container-hosting in the cloud

The rise of containers ● Any language
● Any library
● Any binary
● Ecosystem of base images
● Industry standard
“We can’t be locked in.”
“How can we use
existing binaries?”
“Why do I have to choose between
containers and serverless?”
“Can you support language _______ ?”
Serverless not accessible to everyone...

Code, build, deploy
.js .rb .go
.sh.py ...
● Any language, library, binary
○ HTTP port, stateless
● Bundle into container
○ Build w/Docker OR
○ Google Cloud Build
○ Image ⇒ Container Registry
● Deploy to Cloud Run (native or GKE)
StateHTTP
https://yourservice.run.app
Cloud Run
Fully serverless
No cluster to manage
Pay for what you use
Cloud Run on GKE
Serverless experience
Access custom nodes, GPUs, VPC
Simplicity of Cloud Run
With flexibility of GKE
Fully-managed Kubernetes cluster
Serverless containers, where you want them
Deploy to your
self-managed
Kubernetes cluster
Cloud Run compatible
With Knative open API
Runs on-prem or in other cloud on
Self-managed Kubernetes cluster

4 All of Cloud (inspiration)
Build with both GCP tools and G Suite
Custom intelligence in Gmail
Analyze G Suite data with GCP

Gmail message processing with GCP
Gmail
Cloud
Pub/Sub
Cloud
Functions
Cloud
Vision
G Suite GCP
Star
message
Message
notification
Trigger
function
Extract
images
Categorize
images

Inbox augmented with Cloud Function
● Gmail API: sets up notification forwarding to Cloud Pub/Sub
● developers.google.com/gmail/api/guides/push
● Pub/Sub: triggers logic hosted by Cloud Functions
● cloud.google.com/functions/docs/calling/pubsub
● Cloud Functions: "orchestrator" accessing GCP APIs
● Combine all of the above to add custom intelligence to Gmail
● Deep dive code blog post
● cloud.google.com/blog/products/application-development/
adding-custom-intelligence-to-gmail-with-serverless-on-gcp
● Application source code
● github.com/GoogleCloudPlatform/cloud-functions-gmail-nodejs
App summary

Big data analysis to slide presentation
Access GCP tools from G Suite

Store big data results
Visualize big data results

Ingest data from Sheets
Link to chart in Sheets

Supercharge G Suite with GCP
G Suite GCP
BigQuery
Apps Script
Slides Sheets
Application
request
Big data
analytics

App summary
● Leverage GCP and build the "final mile" with G Suite
● Driven by Google Apps Script
● Google BigQuery for data analysis
● Google Sheets for visualization
● Google Slides for presentable results
● "Glued" together w/G Suite serverless
● Build this app (codelab)
● g.co/codelabs/bigquery-sheets-slides
● Video and blog post
● bit.ly/2OcptaG
● Application source code
● github.com/googlecodelabs/bigquery-sheets-slides
● Presented at Google Cloud NEXT (Jul 2018 [DEV229] & Apr 2019 [DEV212])
● cloud.withgoogle.com/next18/sf/sessions/session/156878
● cloud.withgoogle.com/next/sf/sessions?session=DEV212
Online resources & summary
What's available for students & educators?5

Session Summary
● Why go cloud?
○ Cloud computing has taken the world by storm
○ You're behind if you're not already using it… it's not too late!
○ Help train the next generation cloud-ready workforce!
● Google Cloud and why serverless?
○ Many features: compute, storage, AI/ML, NW, data processing, etc.
○ Serverless lets users focus on just their logic (apps or functions)
○ Interesting possibilities using both platforms (GCP + G Suite)
References
● G Suite & GCP home pages & documentation
○ developers.google.com/gsuite
○ developers.google.com/apps-script
○ github.com/gsuitedevs
● Google Cloud Platform (GCP) documentation & open source repos
○ cloud.google.com/gcp
○ cloud.google.com/docs
○ github.com/GoogleCloudPlatform/{python,nodejs}-docs-samples
○ Know AWS? Compare w/GCP at: cloud.google.com/docs/compare/aws
● Google APIs Client Libraries (G Suite & GCP) & Google Cloud SDK (GCP-only)
○ developers.google.com/api-client-library
○ cloud.google.com/sdk

More references
● Relevant videos
○ goo.gl/RbyTFD (new Google APIs project setup)
○ goo.gl/KMfbeK (common Python OAuth2 boilerplate code review)
○ goo.gl/ZIgf8k (APIs intro codelab [Drive API])
● Relevant codelabs
○ g.co/codelabs/gsuite-apis-intro (Drive API)
○ g.co/codelabs/apps-script-intro
○ codelabs.developers.google.com/codelabs/cloud-app-engine-python
○ codelabs.developers.google.com/codelabs/cloud-starting-cloudfunctions
● Inspirational apps
○ bit.ly/2OcptaG
○ cloud.google.com/blog/products/application-development/
adding-custom-intelligence-to-gmail-with-serverless-on-gcp
○ cloud.withgoogle.com/next/sf/sessions?session=DEV212
Learning resources
● Codelabs: self-paced, hands-on tutorials
○ Google codelabs: need a Gmail account, always free
■ g.co/codelabs/cloud
○ Qwiklabs codelabs: don't need a Gmail acct; typically not free
■ google.qwiklabs.com
■ Puchase credits a la carte, or discounted in-bulk / via subscription
● Official GCP documentation
○ cloud.google.com/gcp/getting-started
○ Recommended: Getting Started, Cloud Console, Cloud Shell, Cloud SDK, Community
● YouTube video series:
○ youtube.com/GoogleCloud
○ Recommended: Cloud Minute shorts & Cloud NEXT videos
○ G Suite Dev Show: goo.gl/JpBQ40

Security in Google Cloud
● Google Cloud Security home page: cloud.google.com/security
● Google Cloud Security whitepaper: bit.ly/2Qb3wXX
● Compliance: stds, regulations, certifications: cloud.google.com/security/compliance
● Transparency report (incl. service disruptions): transparencyreport.google.com
● G Suite Security and Trust (PDF "eBook;" links disabled): bit.ly/2WQ1fnI
● G Suite Encryption whitepaper (PDF): bit.ly/2JrmdGy
● GCP Encryption-at-rest whitepaper (PDF):
cloud.google.com/security/encryption-at-rest/default-encryption
● Google Cloud Encryption-in-transit whitepaper (PDF):
cloud.google.com/security/encryption-in-transit
● Google data centers: google.com/about/datacenters
Thank you! Questions?
Wesley Chun
@wescpy
Progress bars: goo.gl/69EJVw

Cloud computing overview & Technical intro to Google Cloud

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