Powerful Google Cloud tools for your hack

Powerful Google Cloud
tools for your hack
Wesley Chun
Developer Advocate, Google
Adjunct CS Faculty, Foothill College
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
● How Google Cloud can help with your hack
● Can't hurt to learn a bit more about cloud computing
● Cloud skills are in-demand in today's workforce
● Show how to get started with Google Cloud
Cloud computing
& Google Cloud
1
Hosting your
code
2
Google APIs
primer
3
Machine
Learning
4 5
Other APIs to
consider
6
Wrap-up
01
Cloud computing
& Google Cloud
Intro and overview

What is Google Cloud Platform?
Getting things done using someone else’s computers, especially
where someone else worries about maintenance, provisioning, system
administration, security, networking, failure recovery, etc.
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

Theme - Spec/Reqs
Logistics - Design app
Space - Provision HW
Food - Build & serve app
Manage - Manage app
on-prem
(DIY)
IaaS
(Compute Engine)
PaaS
(App Engine/GCF)
SaaS
(G Suite)
Pick theme
Plan party
Find space
Cook
On-call
Pick theme
Plan party
Rent hall
Cook
On-call
Pick theme
Plan party
Rent hall
Hire Caterer
Hire manager
Pick theme
Hire planner
Rent hall
Hire caterer
Hire manager
Imagine you’re hosting a party...
YOUR
NEXT
APP? google.com/datacenter

What is Google Cloud Platform?
GCP lets you host & run code (web apps, mobile
backends, web services, containers), store &
analyze data, and much more, all on Google’s
highly-scalable & reliable computing infrastructure

What is Google Cloud Platform?some Google Cloud Platform products
Compute Big Data
BigQuery
Cloud
Dataflow
Cloud
Dataproc
Cloud
Datalab
Cloud
Pub/Sub
Genomics
Storage & Databases
Cloud
Storage
Cloud
Bigtable
Cloud
Datastore
Cloud SQL
Cloud
Spanner
Persistent
Disk
Machine Learning
Cloud ML
Engine
Cloud
Vision API
Cloud
Speech APIs
Cloud Natural
Language API
Cloud
Translation
API
Cloud
Jobs API
Data
Studio
Cloud
Dataprep
Cloud Video
Intelligence
API
AutoML suite
Compute
Engine
App
Engine
Kubernete
s Engine
GPU
Cloud
Functions
Container-
Optimized OS
Identity & Security
Cloud IAM
Cloud Resource
Manager
Cloud Security
Scanner
Key
Management
Service
BeyondCorp
Data Loss
Prevention API
Identity-Aware
Proxy
Security Key
Enforcement
Internet of Things
Cloud IoT
Core
Transfer
Appliance
Cloud
Firestore
02
Hosting your
code
on GCP serverless/PaaS

What is Google Cloud Platform?some Google Cloud Platform products (tl;dr)
Compute Big Data
BigQuery
Cloud
Dataflow
Cloud
Dataproc
Cloud
Datalab
Cloud
Pub/Sub
Genomics
Storage & Databases
Cloud
Storage
Cloud
Bigtable
Cloud
Datastore
Cloud SQL
Cloud
Spanner
Persistent
Disk
Machine Learning
Cloud ML
Engine
Cloud
Vision API
Cloud
Speech APIs
Cloud Natural
Language API
Cloud
Translation
API
Cloud
Jobs API
Data
Studio
Cloud
Dataprep
Cloud Video
Intelligence
API
AutoML auite
Compute
Engine
App
Engine
Kubernete
s Engine
GPU
Cloud
Functions
Container-
Optimized OS
Identity & Security
Cloud IAM
Cloud Resource
Manager
Cloud Security
Scanner
Key
Management
Service
BeyondCorp
Data Loss
Prevention API
Identity-Aware
Proxy
Security Key
Enforcement
Transfer
Appliance
Cloud
Firestore
Internet of Things
Cloud IoT
Core
>
Google Compute Engine configurable
VMs of all shapes & sizes, from
"micro" to 416 vCPUs, 11.75 TB RAM,
64 TB HDD/SSD plus Google Cloud
Storage for blobs/cloud data lake
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux,
Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016)
cloud.google.com/compute
cloud.google.com/storage
Yeah, we got VMs & big disk… but why?

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

Running Code: App Engine
Got a great app idea? Now what?
VMs? Operating systems? Big disk?
Web servers? Load balancing?
Database servers? Autoscaling?
With Google App Engine, you don't
think about those. Just upload
your code; we do everything else.
>
cloud.google.com/appengine
Why does App Engine exist?

App Engine to the rescue!!
● Focus on app not DevOps
● Enhance productivity
● Deploy globally
● Fully-managed
● Auto-scaling
● Pay-per-use
● Familiar standard runtimes
● 2nd gen std platforms
○ Python 3.7
○ Java 8, 11
○ PHP 7.2
○ Go 1.11
○ JS/Node.js 8, 10
○ Ruby 2.5
Hello World (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:
PROJECT_ID.appspot.com
Quickstart tutorial and open source repo at
cloud.google.com/appengine/docs/standard/python3/quickstart

App Engine demo
Python Flask QuickStart tutorial
(also Java, Node.js, PHP, Go, Ruby)
Running Code: Cloud Functions
Don't have an entire app? Just want
to deploy small microservices or
"RPCs" online globally? That's what
Google Cloud Functions are for!
(+Firebase version for mobile apps)
cloud.google.com/functions
firebase.google.com/products/functions

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
■ Pub/Sub, Cloud Storage, Firebase, etc.
○ Auto-scaling & highly-available; pay per use
● Flexible development environment
○ Cmd-line or developer console (in-browser)
● 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
Quickstart tutorial and open source repo at
cloud.google.com/functions/docs/quickstart-python

No cmd-line
access?
Use in-browser
dev environment!
● setup
● code
● deploy
● test
Cloud Functions demo
Python GCF QuickStart tutorial
(also in Node.js & Go)

Running Code: Cloud Run
Got a containerized app? Want its
flexibility along with the convenience
of serverless that's fully-managed
plus auto-scales? Google Cloud Run is
exactly what you're looking for!
cloud.google.com/run
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 (managed or GKE)
StateHTTP
https://yourservice.run.app

Dockerfile
FROM python:3.7
ENV APP_HOME /app
ENV TARGET MyWorld
WORKDIR $APP_HOME
COPY . .
RUN pip install Flask gunicorn
CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 app:app
cloud.google.com/run/docs/quickstarts/build-and-deploy or
github.com/knative/docs/tree/master/docs/serving/samples/h
ello-world/helloworld-python
.dockerignore
Dockerfile
README.md
*.pyc
*.pyo
*.pyd
__pycache__
app.py
import os
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
target = os.environ.get('TARGET', 'World')
return 'Hello {}!'.format(target)
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))

Build (think docker build):
$ gcloud builds submit --tag gcr.io/PROJ_ID/CONT_NAME
Deploy (think docker push):
$ gcloud run deploy --image gcr.io/PROJ_ID/CONT_NAME
--platform managed
Access globally:
https://CONT_NAME-RANDOM_HASH-uREGION.a.run.app
03
Google APIs
primer
How to access Cloud APIs

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)
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

Navigating the Cloud console
● 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

04
Machine
Learning
Access AI/ML w/API calls
quickdraw.withgoogle.com

Machine learning is learning
from rules plus experience.
rules
data
Traditional
Programming
answers
answers
data
rulesMachine
Learning

Inference Phase
Training Phase
answers
data
rules/modelMachine
Learning
Model
predictions(new) data
Google Translate

Google Photos
Did you ever stop
to notice this app
has a search bar?!?
Full Spectrum of AI & ML Offerings
App developer Data scientist,
developer
Data scientist, Researcher
(w/infrastructure access &
DevOps/SysAdmin skills)
ML EngineAuto ML
Build custom models,
use OSS SDK on fully-
managed infrastructure
ML APIs
App developer,
data scientist
Use/customize pre-built
models
Use pre-built/pre-
trained models
Build custom models, use/
extend OSS SDK, self-manage
training infrastructure

Machine Learning: Cloud Vision
Google Cloud Vision API lets
developers extract metadata and
understand the content of an
image, identifying & detecting
objects/labels, text/OCR,
facial features, landmarks,
logos, products, XC, etc.
cloud.google.com/vision
from google.cloud import vision
IMG = 'https://google.com/services/images/section-work-card-img_2x.jpg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = IMG
response = client.label_detection(image=image)
print('** Labels detected (and confidence score):')
for label in response.label_annotations:
print('%s (%.2f%%)' % (label.description, label.score*100.))
response = client.face_detection(image=image)
print('n** Facial features detected (and likelihood):')
for label in response.face_annotations:
for likelihood in dir(label):
if likelihood.endswith('_likelihood'):
llh = str(vision.enums.Likelihood(getattr(label,
likelihood))).split('.')[1].replace('_', ' ').lower()
print('%s: %s' % (likelihood.split('_')[0].title(), llh))
Vision: image analysis & metadata extraction

$ python3 viz_demo.py
** Labels detected (and confidence score):
Sitting (89.94%)
Interior design (86.09%)
Furniture (82.08%)
Table (81.52%)
Room (80.85%)
White-collar worker (79.04%)
Office (76.19%)
Conversation (68.18%)
Photography (62.42%)
Window (60.96%)
** Facial features detected (and likelihood):
Anger: very unlikely
Blurred: very unlikely
Headwear: very unlikely
Joy: very likely
Sorrow: very unlikely
Surprise: very unlikely
Under: very unlikely
Vision: image analysis & metadata extraction
from google.cloud import vision
image_uri = 'gs://cloud-vision-codelab/otter_crossing.jpg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.text_detection(image=image)
for text in response.text_annotations:
print('=' * 30)
print(f'"{text.description}"')
vertices = [f'({v.x},{v.y})' for v in text.bounding_poly.vertices]
print(f'bounds: {",".join(vertices)}')
Vision: OCR, text detection/extraction

$ python3 text-detect.py
==============================
"CAUTION
Otters crossing
for next 6 miles
"
bounds: (61,243),(251,243),(251,340),(61,340)
==============================
"CAUTION"
bounds: (75,245),(235,243),(235,269),(75,271)
==============================
"Otters"
bounds: (65,296),(140,297),(140,315),(65,314)
==============================
"crossing"
bounds: (151,294),(247,295),(247,317),(151,316)
:
Vision: OCR, text detection/extraction
g.co/codelabs/vision-python
Cloud Vision
exercise
g.co/codelabs/vision-
python
(others at gcplab.me)

Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
reveals the structure and meaning
of text, performing sentiment
analysis, content classification,
entity extraction, and syntactical
structure analysis; multi-lingual
cloud.google.com/language
Simple sentiment & classification analysis
from google.cloud import language
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.'''
NL = language.LanguageServiceClient()
document = language.types.Document(content=TEXT,
type=language.enums.Document.Type.PLAIN_TEXT)
sentiment = NL.analyze_sentiment(document).document_sentiment
print('TEXT:', TEXT)
print('nSENTIMENT: score (%.2f), magnitude (%.2f)' % (
sentiment.score, sentiment.magnitude))
categories = NL.classify_text(document).categories
print('nCATEGORIES:')
for cat in categories:
print('* %s (%.2f)' % (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 (0.30), magnitude (0.60)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)
Machine Learning: Cloud Speech
Google Cloud Speech APIs enable
developers to convert
speech-to-text and vice versa
cloud.google.com/speech
cloud.google.com/text-to-speech

Text-to-Speech: synthesizing 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'
Machine Learning: Cloud Video Intelligence
Google Cloud Video Intelligence
API makes videos searchable, and
discoverable, by extracting
metadata. Other features: object
tracking, shot change detection,
and text detection
cloud.google.com/video-intelligence

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%)
Machine Learning: Cloud Translation
Access Google Translate
programmatically through this
API; translate an arbitrary
string into any supported
language using state-of-the-art
Neural Machine Translation
cloud.google.com/translate

● What is it, and how does it work?
○ Google Cloud ML APIs use pre-trained models
○ Perhaps those models less suitable for your data
○ Further customize/train our models for your data
○ Without sophisticated ML background
○ Translate, Vision, Natural Language, Video Intelligence, Tables
○ cloud.google.com/automl
● Steps
a. Prep your training data
b. Create dataset
c. Import items into dataset
d. Create/train model
e. Evaluate/validate model
f. Make predictions
Cloud AutoML
05
Other APIs to
consider
What else may be useful?

Storing Data: Cloud SQL
SQL servers in the cloud
High-performance, fully-managed
600MB to 416GB RAM; up to 64 vCPUs
Up to 10 TB storage; 40,000 IOPS
Types:
MySQL
Postgres
SQLServer (2019)
cloud.google.com/sql
Storing Data: Cloud Datastore
Cloud Datastore: a fully-
managed, highly-scalable
NoSQL database for your web
and mobile applications
cloud.google.com/datastore

Storing Data: Firebase
Firebase data is stored
as JSON & synchronized in
real-time to every
connected client; other
tools + FB == v2 mobile
development platform
firebase.google.com
Storing Data: Cloud Firestore
The best of both worlds: the
next generation of Cloud
Datastore (w/product rebrand)
plus features from the
Firebase realtime database
cloud.google.com/firestore

● Ordinary database - explicitiy query database for
new updates (but when?)
● Realtime database - automatically receive deltas
when database updated (setup client listener object)
● Highly scalable database
● Multi-regional data replication
● Strong consistency (vs. eventual consistency): read
your writes!
Cloud Firestore key features
Cloud Firestore data model
collections subcollections

Cloud Firestore straightforward querying
github.com/GoogleCloudPlatform/python-docs-samples/blob/master/firestore/cloud-client/snippets.py
Firestore: create & query for objects
from datetime import datetime
from google.cloud import firestore
def store_time(timestamp):
visits = FRSTOR.collection('visit')
visits.add({'timestamp': timestamp})
def fetch_times(limit):
visits = FRSTOR.collection('visit')
return visits.order_by(u'timestamp',
direction=firestore.Query.DESCENDING).limit(limit).stream()
FRSTOR = firestore.Client() # create Cloud Firestore client
print('** Adding another visit')
store_time(datetime.now()) # store a "visit" object
print('** Last 10 visits')
times = fetch_times(10) # fetch 10 most recent "visits"
for obj in times:
print('-', obj.to_dict()['timestamp'])

Storing and Analyzing Data: BigQuery
Google BigQuery is a fast, highly
scalable, fully-managed data
warehouse in the cloud for
analytics with built-in machine
learning (BQML); issue SQL queries
across multi-terabytes of data
cloud.google.com/bigquery
BigQuery: querying Shakespeare words
from google.cloud import bigquery
TITLE = "The 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 = bigquery.Client().query(QUERY).result()
print('n*** Results for %r:n' % TITLE)
print('t'.join(col.name.upper() for col in rsp.schema)) # HEADERS
print('n'.join('t'.join(str(x) for x in row.values()) for row in rsp)) # DATA

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
G Suite: Google Drive
Drive API allows developers to read,
write, control permissions/sharing,
import/export files, and more!
developers.google.com/drive

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
Download from Drive, upload to GCS
# Assume 1) FNAME == Drive filename (and it exists), 2) BUCKET ==
# GCS bucket name, and 3) DRIVE and GCS are API service endpoints
# Find 1st matching file on Google Drive, download binary & info
target = DRIVE.files().list(q="name='%s'" % FNAME,
fields='files(id,mimeType,modifiedTime)').execute().get('files')[0]
data = DRIVE.files().get_media(fileId=target['id']).execute()
print('Downloaded %r (%s, %s, size: %d)' % (FNAME,
target['mimeType'], target['modifiedTime'], len(data)))
# Setup metadata and upload blob/object to Google Cloud Storage
body = {'name': FNAME, 'uploadType': 'multipart', 'contentType': target['mimeType']}
data = http.MediaIoBaseUpload(io.BytesIO(data), mimetype=target['mimeType'])
rsp = GCS.objects().insert(bucket=BUCKET, body=body,
media_body=data, fields='bucket,name').execute()
print('Uploaded %r to GCS bucket %r' % (rsp['name'], rsp['bucket']))

Automate photo albums
OR
G Suite: Google Sheets
Sheets API gives you programmatic
access to spreadsheets; perform
(w/code) almost any action you can
do from the web interface as a user
developers.google.com/sheets

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

Visualize big data results
function createColumnChart(spreadsheet) {
var sheet = spreadsheet.getSheets()[0];
var START_CELL = 'A2'; // skip header row
var END_CELL = 'B11';
var START_ROW = 5; // place chart in...
var START_COL = 5; // row 5, col 5 (E)
var OFFSET = 0;
var chart = sheet.newChart()
.setChartType(Charts.ChartType.COLUMN)
.addRange(sheet.getRange(START_CELL + ':' + END_CELL))
.setPosition(START_ROW, START_COL, OFFSET, OFFSET)
.build();
sheet.insertChart(chart);
return chart;
}
Other Google APIs & platforms
● G Suite (you can code Gmail, Google Drive, Calendar, Docs, Sheets, Slides!)
○ developers.google.com/gsuite
● Firebase (mobile development platform + RT DB)
○ firebase.google.com
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
● Actions on Google/Assistant/DialogFlow (voice apps)
○ developers.google.com/actions
● YouTube (Data, Analytics, and Livestreaming APIs)
○ developers.google.com/youtube
● Google Maps (Maps, Routes, and Places APIs)
○ developers.google.com/maps
● Flutter (native apps [Android, iOS, web] w/1 code base[!])
○ flutter.dev

06
Wrap-up
Summary & resources for
hackers
● Key GCP product code samples for students: goo.gle/hackathon-toolkit
● Google Cloud codelabs (free, self-paced, hands-on tutorials): gcplab.me
● Other Google codelabs: g.co/codelabs
● GCP documentation - cloud.google.com/{docs,appengine,functions,vision,automl,
language,speech,text-to-speech,translate,video-intelligence,firestore,bigquery}
● Like GCP? Wanna use it in class or your research lab? Send your profs to
cloud.google.com/edu to apply for teaching or research credits!
● Know AWS? Compare w/GCP at cloud.google.com/docs/compare/aws
● Other references
○ Firebase docs - firebase.google.com
○ G Suite docs - developers.google.com/{gsuite,drive,docs,sheets,slides}
○ Videos - youtube.com/GoogleCloudPlatform (GCP) & goo.gl/JpBQ40 (G Suite)
○ Free trial (ignore) and Always Free (tier) - cloud.google.com/free
Resources (students)

Best use of Google
Cloud challenge
Use any Google Cloud affiliated product to
qualify; GCP, G Suite, Firebase, Maps, etc.
Eligible products @ cloud.google.com/products
Every member of the winning team gets:
● Google Home Mini
● Patagonia Backpack
● Cloud Pillow
● Acrylic Trophy
● Water Bottle
Best use of Google
Cloud challenge
Use any Google Cloud affiliated product to
qualify; GCP, G Suite, Firebase, Maps, etc.
Eligible products @
cloud.google.com/products
Every member of the runner-up team
receives:
● Google Home Mini

$50 in GCP credits
Check for an email from Major League
Hacking (MLH) to activate your coupon
for free $50USD worth of GCP credits!
(NOTE: must exceed Always Free tier to
incur billing, so you may not use much)
WARNING: avoid the GCP $300 free trial!! 💳 😓
Thank you! Qs?
Hit us up on Slack or Discord
Wesley Chun
@wescpy
Progress bars: goo.gl/69EJVw

Powerful Google Cloud tools for your hack

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