This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

1. Sudbury :: Spr 2024
Wesley Chun
Principal, CyberWeb
@wescpy@
Powerful Google developer
tools for immediate impact!
Where to run your code, store your data, and analyze your data
Principal Consultant, CyberWeb
● Mission: produce accelerated Python
developers, enable developers to be
successful using Google Cloud and
other Google developer tools & APIs
● Focus: Python, Google Cloud (GCP) &
Google Workspace (GWS) APIs; GAE
migrations; Google X-product sol'ns
● Services: technical consulting,
training, engineering, speaking, code
samples, hands-on tutorials, public
technical content (blogs, social, etc.)
About the speaker
Previous experience / background
● Software Engineer & Developer Advocate
○ Google, Sun, HP, Cisco, EMC, Xilinx &
○ Original Yahoo!Mail engineer/SWE
● Technical trainer, teacher, instructor
○ Teaching Math, Linux, Python since '83
○ Adjunct CS Faculty at local SV colleges
● Python community member
○ Popular Core Python series author
○ Python Software Foundation Fellow
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)
GWS Dev Show
goo.gl/JpBQ40
GAE migration
bit.ly/3xk2Swi

2. Impactful developer tools: why & agenda
● People know Google for search, YouTube, Chrome, Android, Gmail
● Google also has an extensive set of developer tools, APIs & platforms
● Many of these developer services part of Google Cloud
● Cloud computing has taken industry by storm & its skills are in-demand
● Look at most impactful tools for work/school projects you can use today!
Cloud computing
& Google Cloud
1
Where to run
your code
2
Google APIs
primer
3
Where to
analyze your
data
4 5
Where to store
your data
7
Wrap-up
6
Inspiration
01
Cloud computing
& Google Cloud
Intro and overview

3. Getting things done using someone else’s computers, especially
where someone else worries about maintenance, provisioning, system
administration, security, networking, failure recovery, upgrades, etc.
Google Compute Engine, 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, Vertex AI,
Cloud Datastore, NL, Vision, Pub/Sub
AWS Kinesis, RDS; Windows Azure SQL, Docker
Google Apps Script
Salesforce Platform (formerly force.com)
Google Workspace (was G Suite/Google Apps)
Yahoo!Mail, Hotmail, Salesforce, Netsuite, Office 365
Google App Engine, Cloud Functions
Heroku, Cloud Foundry, Engine Yard, AWS Lambda

4. Summary of responsibility
SaaS
Software as a Service
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
IaaS
Infrastructure as a Service
Applications
Data
Runtime
Middleware
OS
Virtualization
Servers
Storage
Networking
PaaS
Platform as a Service
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)
SaaS
(Google Workspace
(formerly 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...

5. formerly
( )
02
Where to run
your code
Serverless simplifies it all!

6. > Google Compute Engine configurable
VMs of all shapes & sizes, from
"micro" to 416 vCPUs, 11.776 TB
RAM, 256 TB HDD/SSD plus Google
Cloud Storage for data lake "blobs"
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux,
Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016, 1803,
1809, 1903/2019, 1909)
cloud.google.com/compute
cloud.google.com/storage
Yeah, there are VMs & big disk… but why*?
Serverless: what & why
● What is serverless?
○ Misnomer (a "PMM") :-)
○ "No worries"
○ Developers focus on writing code & solving business problems*
○ Servers (physical & virtual) completely abstracted away from the user*
● Why serverless?
○ Fastest growing segment of cloud... per analyst research:
■ $1.9B (2016) & $4.25B (2018) ⇒ $7.7B (2021), $14.93B (2023), and $21.1B (2025)^
■ $4.18B (2018) & $6.05B (2020) ⇒ $31.53B (2026), $53.08B (2028), and $86.94B (2030)†
○ What if you go viral? Autoscaling: your new best friend
○ What if you don't? Code not running? You're not paying.
* Forbes (May 2018)
^ (in USD) CB Insights (Sep 2018), MarketsandMarkets™ (Jan 2019)
† (in USD) Reports and Data (Jan 2020, Oct 2021, and Jun 2022)

7. Google Compute Engine, Google Cloud Storage
AWS EC2 & S3; Rackspace; Joyent
SaaS
Software as a Service
PaaS
Platform as a Service
IaaS
Infrastructure as a Service
Google Workspace (was G Suite/Google Apps)
Yahoo!Mail, Hotmail, Salesforce, Netsuite, Office 365
Google App Engine, Cloud Functions
Heroku, Cloud Foundry, Engine Yard, AWS Lambda
Google BigQuery, Cloud SQL,
Cloud Datastore, NL, Vision, Pub/Sub
AWS Kinesis, RDS; Windows Azure SQL, Docker
Serverless: PaaS-y compute/processing
Google Apps Script
Salesforce Platform (formerly force.com)
Google App Engine
App-hosting in the cloud ("Serverless 1.0")
cloud.google.com/appengine

8. Why does App Engine exist?
● Focus on code not DevOps
○ Web app or mobile backend
● Enhance productivity
● Deploy globally
● Fully-managed
● Auto-scaling
● Pay-per-use
● Familiar languages
● Test w/local dev server
● "Bundled" services like DB,
caching, tasks, storage, etc.
Hello World (Python "MVP")
app.yaml
runtime: python39
main.py
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello World!'
requirements.txt
flask
Deploy:
$ gcloud app deploy
Access globally:
PROJECT_ID.appspot.com
cloud.google.com/appengine/docs/standard/python3/quickstart

9. Google Cloud Functions
Function-hosting in the cloud ("Serverless 2.0")
cloud.google.com/functions
Why does Cloud Functions exist?
● Don't have entire app?
○ No framework "overhead" (LAMP, MEAN...)
○ Deploy short utilities (alerts, ETL...), monoliths →
loosely-coupled microservices, event-driven tasks
● 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)
○ Develop/test locally with Functions Framework
● Cloud Functions for Firebase
○ Mobile app use-cases

10. main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python39 --trigger-http
Access globally (curl):
$ curl REGION-PROJECT_ID.cloudfunctions.net/hello
Access globally (browser):
https://REGION-PROJECT_ID.cloudfunctions.net/hello
Hello World (Python "MVP")
cloud.google.com/functions/docs/quickstart-python
Google Cloud Run
Container-hosting in the cloud ("Serverless 2.0")
cloud.run

11. The rise of containers... ● Any language
● Any library
● Any binary
● Ecosystem of base images
● Industry standard
FLEXIBILITY
“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 inaccessible for some...
CONVENIENCE

12. Cloud Run: code, build, deploy .js .rb .go
.sh
.py ...
● Any language, library, binary
○ HTTP port, stateless
● Bundle into container
○ Build with Docker OR Cloud Build
○ Image ⇒ Container/Artifact Registry
● Deploy to Cloud Run (managed or GKE)
● GitOps: CD push-to-deploy from Git
○ See documentation & announcement
○ CI/CD with GitHub Actions tutorial
● Cloud Buildpacks: Docker, Dockerfiles,
and knowledge of containers optional
State
HTTP
Hello World (Python "MVP")
main.py
import os
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
cloud.google.com/run/docs/quickstarts/build-and-deploy
requirements.txt
flask

13. Hello World (Python "MVP")
Dockerfile
FROM python:3-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]
.dockerignore
Dockerfile
README.md
*.pyc
*.pyo
.git/
__pycache__
Build (think docker build and docker push) then deploy (think docker run):
$ gcloud builds submit --tag gcr.io/PROJ_ID/IMG_NAME
$ gcloud run deploy SVC_NAME --image gcr.io/PROJ_ID/IMG_NAME
OR… Build and Deploy (1-line combination of above commands):
$ gcloud run deploy SVC_NAME --source .
Access globally:
SVC_NAME-HASH-REG_ABBR.a.run.app
Docker &
Dockerfile
OPTIONAL!!
● Build containers easily & securely without creating/managing Dockerfiles
● Open source, open standard; based on CNCF Buildpacks spec v3
● Used by GCF Functions Framework to deploy locally-developed functions
● Supports most common development tools
○ Go 1.10+
○ Node.js 10+
○ Python 3.7+
○ Java 8 & 11
○ .NET Core 3.1+
● Blog posts
○ See cloud.google.com/blog/products/containers-kubernetes/google-cloud-now-supports-buildpacks and
cloud.google.com/blog/products/serverless/build-and-deploy-an-app-to-cloud-run-with-a-single-command
Deploy to Cloud Run with Buildpacks
github.com/GoogleCloudPlatform/buildpacks
$ ls
index.js package.json
$ gcloud run deploy myapp --source .
$ ls
main.py requirements.txt
$ gcloud run deploy myapp --source .
Node.js, Go,
Python, etc.?
It's the same
command!

14. Flexibility in options
Cloud
Functions
App
Engine
Cloud
Run
local
server
● "Nebulous" sample web app
○ Flask/Python 2 or 3
○ Express/Node.js 10+
○ Uses Cloud Translation API
● Deployable to on-prem server
● Also GCP serverless compute
○ App Engine
○ Cloud Functions
○ Cloud Run
● With only config changes
● No changes to app code
Cloud
Translation
My "Google Translate" MVP
goo.gle/2Y0ph5q
youtu.be/eTotLOVR7MQ
Google Apps Script
Customized serverless JS runtime for automation, and extension
and integration with Google Workspace (formerly G Suite) and
other Google & external services

15. “Hello World!” in Apps Script
Sheets-bound “Hello World!”
Apps Script intro
goo.gl/1sXeuD
t.co/rqHT2gYN6z

16. 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
g.co/codelabs/apps-script-intro

17. 03
Google APIs primer
How to access & use Google APIs

19. General steps
1. Go to Cloud Console
2. Login to Google/Gmail account
(Workspace domain may require admin approval)
3. Create project (per application)
4. Enable APIs to use
5. Enable billing (CC, Free Trial, etc.)
6. Download client library(ies)
7. Create & download credentials
8. Write code*
9. Run code (may need to authorize)
Google APIs: how to use
*In your code
1. Import API client library
2. Create API client object
3. Use client to make API Calls
Costs & pricing
● GCP & GMP: pay-per-use (CC req'd)
● GWS: "subscription" (incl. $0USD/mo.)
● GMP: $200/mo. free usage
● GCP Free Trial: $300/1Q
● GCP "Always Free" tier
○ Some products have free tier
○ Daily or monthly quota
○ Must exceed to incur billing
● More on both programs at
cloud.google.com/free

20. 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

21. Client libraries and credentials types
● Two different client library types
○ Platform-level client libraries (lower-level)
■ Multiple product groups as a "lowest-common denominator"
■ Install: developers.google.com/api-client-library
○ Product-level client libraries (higher-level)
■ Custom client libraries made specifically for 1 product or product group
■ Found on product or product group page(s)
● Three different credentials types
○ Simple: API keys (access public data)
■ Simplest form of authorization: an API key; tied to a project
○ Authorized: OAuth client IDs (access data owned by [human] user)
■ Provides additional layer of security via OAuth2 (RFC 6749)
○ Authorized: service accounts (access data owned by an app/robot user)
■ Provides additional layer of security via OAuth2 or JWT (RFC 7519)
Blog series:
dev.to/wescpy
&
Google APIs client
libraries for many
languages; demos in
developers.google.com/api-
client-library
cloud.google.com/apis/docs
/cloud-client-libraries

22. 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)
04
How to analyze
your data
AI/ML services via API

23. AI & machine learning
Puppy or muffin?
Source: twistedsifter.com/2016/03/
puppy-or-bagel-meme-gallery

24. Machine learning is learning
from rules plus experience.
Inference Phase
Training Phase
answers
data
model ("rules")
Machine
Learning
Model
predictions
(new) data

25. Vertex AI task-specific models: ML "building block" APIs
● Gain insights from data using GCP's
pre-trained machine learning models
● Leverage the same technology as Google
Translate, Photos, and Assistant
● Requires ZERO prior knowledge of ML
● If you can call an API, you can use AI/ML!
● cloud.google.com/products/ai/building-blocks
Vision Video
Intelligence
Speech
(S2T & T2S)
Natural
Language
Translation
Machine Learning: Cloud Vision & Video Intelligence
Google Cloud Vision & Video
Intelligence APIs enable developers
to extract metadata & understand the
content of images & videos, making
them searchable & discoverable.
cloud.google.com/vision
cloud.google.com/video-intelligence

26. from google.cloud import vision
image_uri = 'gs://cloud-samples-data/vision/using_curl/shanghai.jpeg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.label_detection(image=image)
print('Labels (and confidence score):')
print('=' * 30)
for label in response.label_annotations:
print(label.description, '(%.2f%%)' % (label.score*100.))
Vision: label annotation/object detection
$ python3 label-detect.py
Labels (and confidence score):
==============================
People (95.05%)
Street (89.12%)
Mode of transport (89.09%)
Transport (85.13%)
Vehicle (84.69%)
Snapshot (84.11%)
Urban area (80.29%)
Infrastructure (73.14%)
Road (72.74%)
Pedestrian (68.90%)
Vision: label annotation/object detection
g.co/codelabs/vision-python

27. Google Photos
The app has a
search bar?!?
blog.google/products/photos/smarter-photo-albums-without-work
Global view
Problem
● 1B ppl depend on seafood
● 85% at/over-fishing or recovering
● 20% caught illegal, undoc'd, unreg'd
● Analysts monitoring unscalable
One solution
● globalfishingwatch.org/map
● Machine-learning classifiers:
○ Ship type: cargo, tug, sail, fishing
○ Ship size
○ Gear: longline, purse seine, trawl
○ Movement tracking: when and
where vessels are fishing

28. quickdraw.withgoogle.com
Cloud Vision demo: Quick Draw game
experiments.withgoogle.com/quick-draw
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

29. 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)
print('TEXT:', TEXT) # sentiment analysis
sent = NL.analyze_sentiment(document).document_sentiment
print('nSENTIMENT: score (%.2f), magnitude (%.2f)' % (sent.score, sent.magnitude))
print('nCATEGORIES:') # content classification
categories = NL.classify_text(document).categories
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 (0.20), magnitude (0.50)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)

30. Machine Learning: AutoML
AutoML: suite of cloud APIs for
developers with limited machine
learning expertise; take task-specific
pre-trained model, perform additional
training with your data to finetune
that model
(Translation, Vision, Natural Language,
Video Intelligence, Tables)
cloud.google.com/automl
cloud.google.com/automl-tables
import google.generativeai as genai
from settings import API_KEY
PROMPT = 'Describe a cat in a few sentences'
MODEL = 'gemini-pro'
print('** GenAI text: %r model & prompt %rn' % (
MODEL, PROMPT))
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel(MODEL)
response = model.generate_content(PROMPT)
print(response.text)
Google AI: Gemini API
ai.google.dev/tutorials
Blog series:
dev.to/wescpy

31. Full Spectrum of AI & ML Offerings
App developer Data scientist,
developer
Data scientist, Researcher
(w/infrastructure access &
DevOps/SysAdmin skills)
Vertex AI
platform
Build custom models,
use OSS SDK on fully-
managed infrastructure
Single-task
model APIs
App developer,
data scientist
Use or fine-tune
pre-built models
Use pre-built/pre-
trained models
Build custom models, use/
extend OSS SDK, self-manage
training infrastructure
LMM/large
multimodal
model API
Auto ML
Google and Jupyter Notebooks
Users have many ways to access an indispensable data science tool
● Jupyter Notebook support on GCP
○ cloud.google.com/notebooks
○ Vertex AI Workbench
■ cloud.google.com/vertex-ai/docs/workbench
○ Colab Enterprise
■ cloud.google.com/colab
● Kaggle
○ kaggle.com
● Google Research CoLaboratory (Colab)
○ colab.research.google.com
● GCP-deprecated: Dataproc Hub, Datalab, Vertex AI managed & user-mgd

32. 05
Where to store
your data
And more ways to analyze it
Storage and Big Data
Cloud Storage & Filestore Object/file-based storage
Cloud SQL, AlloyDB, Spanner FM relational databases
Cloud Datastore, BigTable FM scalable NoSQL DBs
Firebase Realtime JSON/document store
Cloud Firestore Document DB (Datastore + Firebase)
BigQuery Data warehouse & analysis
Cloud Pub/Sub Message queue service

33. Storing Data: Cloud Firestore
A fully-managed, highly-scalable
NoSQL database for your web and
mobile applications, combining
Cloud Datastore with features from
the Firebase realtime database
(For choosing between Firebase & Cloud Firestore: see
firebase.google.com/docs/firestore/rtdb-vs-firestore;
for choosing between Firestore Datastore & Firestore Native modes:
see cloud.google.com/datastore/docs/firestore-or-datastore)
cloud.google.com/firestore
Cloud Firestore: data model
collections subcollections

34. Cloud Firestore: straightforward queries
github.com/GoogleCloudPlatform/python-docs-samples/blob/master/firestore/cloud-client/snippets.py
Storing and Analyzing Data: BigQuery
Google BigQuery: fully-managed data
warehouse for large-scale data
analytics with built-in machine
learning (BQML); issue SQL queries
across multi-terabytes of data. BQ
Sandbox lets anyone query up to
1TB/mo for free with no obligation
cloud.google.com/bigquery

35. BigQuery: querying Shakespeare words
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 = BQ.jobs().query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
print('t'.join(col['name'].upper() # HEADERS
for col in rsp['schema']['fields']))
print('n'.join('t'.join(str(col['v']) # DATA
for col in row['f']) for row in rsp['rows']))
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

36. ● BigQuery public data sets: cloud.google.com/bigquery/public-data
● BQ sandbox (1TB/mo free): cloud.google.com/bigquery/docs/sandbox (see blog post)
● Other public data sets: cloud.google.com/public-datasets (Google Cloud),
research.google/tools/datasets (Google Research), and Kaggle (kaggle.com)
● COVID-19 BigQuery data sets
○ How to use our data sets (see blog post)
○ JHU Coronavirus COVID-19 Global Cases data set
○ List of all COVID-19 data sets
● Cloud Life Sciences API: cloud.google.com/life-sciences (see blog post)
● Cloud Healthcare API: cloud.google.com/healthcare (see blog post)
BigQuery and public data sets
Google Workspace: 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

37. 06
Inspiration
Use multiple Google APIs
to create unique solutions
Custom intelligence in Gmail
Analyze Google Workspace (formerly G Suite) data with GCP

38. Gmail message processing with GCP
Gmail
Cloud
Pub/Sub
Cloud
Functions
Cloud
Vision
Workspace
(formerly G Suite)
GCP
Star
message
Message
notification
Trigger
function
Extract
images
Categorize
images

39. 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 (and Google Workspace/G Suite) 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

40. Big data analysis to slide presentation
Access GCP tools from Google Workspace (formerly G Suite)

41. Store big data results
Visualize big data results

42. Ingest data from Sheets
Link to chart in Sheets

43. Supercharge Workspace (G Suite) with GCP
Workspace (G Suite) GCP
BigQuery
Apps Script
Slides Sheets
Application
request
Big data
analytics

44. App summary
● Leverage GCP and build the "final mile" with Google Workspace (formerly G Suite)
● Driven by Google Apps Script
● Google BigQuery for data analysis
● Google Sheets for visualization
● Google Slides for presentable results
● "Glued" together w/Google Workspace (formerly 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
07
Wrap-up
Summary & resources

45. Summary: Google developer tools & APIs
● Google provides more than just apps
○ More than search, YouTube, Android, Chrome, Gmail, and Docs
○ Google technology & integration available to developers via APIs
● Tour of Google (Cloud) APIs & developer tools
○ GWS: not just a set of productivity apps… you can code them too!
○ GCP: compute, storage, networking, security, data & machine learning tools
■ Serverless: frees developers from infrastructure
■ So you can focus on building solutions
● Interesting possibilities when using >1 Google product families/APIs
● With Google: run your apps, analyze & store your data
Other Google APIs & platforms
● Google Workspace (G Suite) (code Gmail, Drive, Docs, Sheets, Slides!)
○ developers.google.com/gsuite
● Firebase (mobile development platform and RT DB plus ML-Kit)
○ firebase.google.com and firebase.google.com/docs/ml-kit
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
○ goo.gle/datastudio-course
● 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

46. ● Documentation (most APIs have "Quickstarts")
○ GCP: cloud.google.com/{docs,appengine,functions,run,vision,automl,translate,language,
speech,texttospeech,video-intelligence,firestore,bigquery,compute,storage,gpu,tpu}
○ GWS: developers.google.com/{gsuite,drive,calendar,gmail,docs,sheets,slides,apps-script}
● Introductory "codelabs" ([free] self-paced, hands-on tutorials)
○ GWS APIs: g.co/codelabs/gsuite-apis-intro (featuring Drive API)
○ Apps Script: g.co/codelabs/apps-script-intro
○ App Engine: codelabs.developers.google.com/codelabs/cloud-app-engine-python
○ Cloud Functions: codelabs.developers.google.com/codelabs/cloud-starting-cloudfunctions
○ Cloud Run: codelabs.developers.google.com/codelabs/cloud-run-hello-python3
○ Others: g.co/codelabs (all Google codelabs) and g.co/codelabs/cloud (GCP-only)
● Videos: youtube.com/GoogleCloudPlatform (GCP) and goo.gl/JpBQ40 (GWS)
● Code samples: github.com/GoogleCloudPlatform (GCP) and github.com/googleworkspace (GWS)
● Cloud Free Trial (new users) and Always Free (daily/monthly tier) programs: cloud.google.com/free
● Know AWS/Azure? Compare with GCP products at cloud.google.com/docs/compare/aws
● Language support: cloud.google.com/{python,java,nodejs,go,php,ruby,dotnet}
Resources (industry)
● Google Cloud (GCP)
○ Code samples for students: goo.gle/hackathon-toolkit
○ Videos: youtube.com/GoogleCloudPlatform
○ Codelabs (free, self-paced, hands-on tutorials): g.co/codelabs/cloud
○ Cloud Skills Boost/QwikLabs (free only for students): go.qwiklabs.com/hey-student
● GCP docs/quickstarts - cloud.google.com/{docs,appengine,functions,run,vision,automl,translate,
language, speech,texttospeech,video-intelligence,firestore,bigquery,compute,storage,gpu,tpu}
● Like GCP? Wanna use it in class or your research lab? Send your profs to cloud.google.com/edu to
apply for GCP teaching/research or Cloud Skills Boost/Qwiklabs/training credits!
● Know AWS/Azure? Compare w/GCP at cloud.google.com/docs/compare/aws
● GCP Free Trial (ignore) and Always Free (tier) - cloud.google.com/free
● Mobile apps: Firebase - firebase.google.com and Flutter - flutter.dev
● Workspace - developers.google.com/{workspace,drive,docs,sheets,slides} and goo.gl/JpBQ40
● Other Google (non-GCP) codelabs: g.co/codelabs
● Developer Student Clubs - g.co/dev/dsc - Global Google student developer groups
● Internships & other career stuff - google.com/students
Resources (students)

47. Prepare for careers in a cloud-first
world. Ask your faculty to apply now.
g.co/edu/cloudready
Career Readiness
track: certification
On-demand
Get free access to Architecting
with Google Compute Engine
specialization on-demand on
Coursera.
(100% discount)
Self-paced labs
Get free access to relevant
Google Cloud quests & other
Cloud Skills Boost self-paced
labs powered by Qwiklabs.
(100% discount: 10+ labs)
Google Cloud Certified
Prepare to take the exam and
earn your Cloud certification.
(100% discount for faculty, 50% discount for
students per adherence to timelines and
other criteria)
Careers at Google (and other tech)
Software Engineer (SWE): not the only technical role @Google
● Application Engineer
● Business Analyst
● Developer Advocate (DA)
● Developer Programs Engineer (DPE)
● Hardware Engineer
● Mechanical Engineer
● Network Engineer
● Product Manager (PM)
● Program Manager (PgM or TPM)
● Quantitative Analyst (Quant)
● Release Engineer
● Research Scientist
● Security Engineer
● Site Reliability Engineer (SRE)
● Software Engineer in Test (SET)
● System Administrator
● Technical Writer (TW)
● UX Content Strategist
● UX Designer
● UX Engineer
● Student careers: google.com/students
● Technical development guide: g.co/techdevguide
● Sample technical interview

48. Bring me to your organization
... it is my job to help you!
● Engineering consulting
● Software development
● Technical seminars/tech talks
● Hands-on workshops
● Technical training courses
● Migration strategy & planning
● cyberwebconsulting.com
● appenginemigrations.com
Slides
you're looking
at them now
Work
cyberwebconsulting.com
Books
corepython.com
Blog
dev.to/wescpy
GWS+ML+Svrls
(long multi-topic video)
youtu.be/HFtEypXpoaY
Nebulous serverless
goo.gle/2Y0ph5q
youtu.be/eTotLOVR7MQ
Big data ⇒ slides
bit.ly/2O9KqDd
Progress bars
goo.gl/69EJVw
Thank you! Questions?
Wesley Chun
Principal Consultant, CyberWeb
Python, GCP & GWS specialist
CONTACT: @wescpy@