This document discusses Google Cloud Platform and its data and analytics capabilities. It begins by explaining the evolution of cloud computing models from virtualized data centers to true on-demand cloud services. It then highlights some of Google Cloud Platform's key differentiators like true cloud economics, future-proof infrastructure, access to innovation, and Google-grade security. The document provides overviews of Google Cloud Platform's storage, database, big data, and machine learning offerings and common use cases for each. It also showcases some of Google's innovations in data analytics and machine learning technologies.

1. Build What’s NEXT
Google Cloud Platform: Data & Analytics
Chris Jang (장혜덕), Head of Cloud Platform, Korea
chrisjang@google.com

2. Virtualized
Data Centers
Standard virtual kit for
Rent. Still yours to
manage.
2nd
Wave
Colocation
1st
Wave
Your kit, someone
else’s building.
Yours to manage.
Assembly required True On Demand Cloud
Next
Storage Processing Memory Network
Clusters
Distributed Storage, Processing
& Machine Learning
Containers
3rd
Wave
An actual, global
elastic cloud
Invest your energy in
great apps.

3. Confidential & ProprietaryGoogle Cloud Platform 3
What Makes Google Cloud Platform Different

4. Google Cloud Platform 4
Foundation
Infrastructure & Operations
Data Services
Application
Runtime Services
Enabling No-Touch Operations
Breakthrough Insights,
Breakthrough Applications
The Gear that Powers Google

5. Google Cloud Platform 5
Where Google Cloud Platform Really Makes the Difference
True cloud
economics
Make the cloud work for
you, not your vendor.
• Per-minute pricing
• Sustained use discounts
• Custom machine types;
right infrastructure at
right cost
• No up-front fees or lock-
ins
Future-proof
infrastructure
Scale your business
smoothly and
responsibly.
• Live migration
• Private network, owned
fibre
• Carbon-neutral operation
• Deep investments in
open source
Access to
innovation
Unique Data Platform
• Fully managed services
let you focus on insight,
not infrastructure
• Industry-leading
machine learning
Container Revolution
• Decade of innovation at
managing containers at
scale
• Broad industry
partnerships
Google-grade
security
The best security
available, because we
need it.
• Ongoing investment and
innovation in physical
and electronic
countermeasures
• Over 500 security
engineers building
technology.
• Google controls its cloud
stack, from silicon up.

6. True Cloud Economics

7. Building what’s next 7
77 edge locations
33 countries
The broadest reaching network
of any Cloud Provider
Google-Grade Networking

8. Google Cloud Platform 8
Open Innovation
is what we’ve
always been about.
● Open APIs
● Active contribution across many
open source projects
● Deep history of academic
publishing
● Interoperability

9. Building what’s next 9
Source: google.com/green
Google data centers use half the
energy of typical data centers.
Green is Good
A better cloud
that's better for the
environment.

10. Green is Good
Google uses
35% renewable energy.
Source: google.com/green

11. From traditional batch processing to rock-solid event delivery to the nearly
magical abilities of BigQuery, building on Google’s data infrastructure
provides us with a significant advantage where it matters the most.
Nicholas Harteau
VP of Engineering and Infrastructure
”“

12. Confidential & ProprietaryGoogle Cloud Platform 12

13. Google’s Mission
Organize the world’s information and make
it universally accessible and useful.“
”

14. 2012 20132002 2004 2006 2008 2010
GFS
MapReduce
BigTable Colossus
Dremel Flume
Megastore
Spanner
Millwheel
PubSub
F1
Google Research Publications referenced are available here: http://research.google.com/pubs/papers.html
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 2009 http://research.google.com/pubs/pub35290.html
2016
Dataflow
TensorFlow
Google’s Innovations In Data Analytics

15. Storage and Databases Big Data and Analytics Machine Learning
Google Cloud Platform - Data & Analytics Products
BigQuery
Cloud Dataflow
Cloud Dataproc
Cloud Pub/Sub
Cloud Datalab
Cloud Storage
Cloud Datastore
Cloud Bigtable
Cloud SQL
Cloud ML
Cloud Translate API
Cloud Vision API
Cloud Speech API

16. Common Use Cases For Data & Analytics
“A big data platform”
“A cloud database
for my apps”
“A solution for building
intelligence into my apps”
“A cost-effective
storage solution”

17. 01 Storage

18. Google Cloud Storage
Big Data
Images
Archives
Backups ● Continuous performance
improvements
● Running at Google scale
○ Several PBs ingested and
downloaded daily
○ Same back-end as Google Drive,
Photos, etc.
● Integrated with leading enterprise
backup and archival products

19. Archival storage without compromises
Cloud Storage Nearline
Nearline Online Offline
Time to first byte
Storage cost
(GB/Mth)
0.1s 3 s 3 hrs+
$0.01
$0.02
$0.03
0

20. Google Cloud Storage Partner Ecosystem

21. 02 Databases

22. How Do Our Customers Store Their Data?
Developer Tools
Google Container Engine
Google App Engine
Google Compute Engine

23. NoSQLRelational
Cloud SQL
Good for:
Structured and unstructured
files or binary data
Such as:
Images, large media files,
backups
Good for:
App Engine,
mobile/web
Such as:
User profiles,
catalogs
Good for:
Web frameworks,
existing applications
Such as:
User credentials, customer
orders
Good for:
Heavy read &
write, events
Such as:
AdTech,
Financial, IoT
The Right Data Stores for Each Application
Object/File
Cloud
Bigtable
Cloud
Datastore
Cloud Storage

24. 03 Big Data

25. Big Data Use Cases
Marketing Analytics Mobile Gaming
Analytics
Sensor Data and
IoT
Genomics

26. Google App
Engine
Google
Analytics
Premium
Cloud
Pub/Sub
BigQuery Storage
(tables)
Cloud Bigtable
(NoSQL)
Cloud Storage
(files)
Cloud Dataflow
BigQuery Analytics
(SQL)
Capture Store Analyze
Cloud
Logs
Batch
Process
Stream
Cloud
Monitoring
Cloud
Dataflow
Use
Data
Scientists
Analysts
Cloud Dataproc
Cloud Datalab
Big Data Lifecycle
Real-time analytics
Real-time
dashboard
Real-time
alerts

28. Cloud Datalab

29. 04 Machine Learning

30. Machine Learning: See, Hear and Understand

31. Machine Learning Use Cases
Structured Data
Classification/ Regression
• Customer Churn Analysis
• Product Diagnostics
• Forecasting
Recommendation
• Content Personalization
• Product X-Sells/Up-sells
Anomaly Detection
• Fraud Detection
• Asset Sensor Diagnostics
• Log Metric Anomalies
Unstructured Data
Image Analytics
• Identify damaged shipments
• Explicit Content Classification
• Identify “styles” in images
Text Analytics
• Call Center log analysis
• Language Identification
• Topic Classification
• Sentiment Analysis

32. TensorFlow
● Deep Learning technology powering
over 100 Google services
● Generalizable to vision, sound, text,
video and other data
● Runs on CPUs or GPUs, desktop,
server, or mobile computing
platforms
● Available as Apache 2.0 OSS license

33. Cloud Vision API

34. To get the most
out of data and secure
competitive advantage.
Empower users to make
better decisions
Transform your organization
into a truly data driven company.
Putting tools into hands of
domain experts.
Apply machine learning
broadly and easily
We make it simple and practical
to incorporate machine learning
models within custom
applications.
We’ve “automated out” the
complexity of building and
maintaining data and
analytics systems.
Spend less on ops and
administration
Easily incorporate context
and real-time data into apps
and architectures
Why Google?

35. Building what’s next 35
Average age of a
company joining
the S&P 500
1957 2003 2013
75years
25years
10years

36. Build What’s NEXT
감사합니다!