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Google Cloud Technologies Overview 
Chris Schalk 
Google Developer Advocate 
2
What is 
cloud 
compu/ng? 
3
Just Kidding ;‐) 
Google Cloud Development Technologies
Google BigQuery  Google  
Predic/on API 
Google Storage 
Google App Engine 
Google App Engine for Business (new) 
ExisAng 
New! 
•  Part I Intro to App Engine 
•  App Engine Details 
•  Development Tools 
•  App Engine for Business 
• Part II – Google’s new cloud technologies 
Agenda 
6 
IaaS 
APaaS 
SaaS 
Source: Gartner AADI Summit Dec 2009 
Cloud Computing as Gartner Sees It
7 
IaaS value proposition… 
8 
The PaaS value proposition
• Easy to build 
• Easy to maintain 
• Easy to scale 
9
Google App Engine 
10
500M+ 
daily 
Pageviews 
250,000+ 
Developers 
100,000+ 
Apps 
10
By the numbers 
11 
Some App Engine Partners 
12 
App Engine @ 
GiSag.com 
NexTuesday.com Twelpforce.com 
13 
>62M Users 
Social Networking at Scale 
gigy   Socialize 
14 
gigy   Socialize 
15 
"Although we typically host all our services in‐house,  
on our own infrastructure, we felt that GAE would be a beer fit  
for the live chat feature because of its unique traffic paern, which  
is characterized by very low traffic most of the Ame with very high  
bursts during high profile events." 
Raviv Pavel, Gigya VP of Research and Development 
hp://googleappengine.blogspot.com/2010/02/scalability‐means‐flexibility.html#links 
Flexible Scalability 
App Engine Details 
16
Cloud development in a box
17
•  Downloadable SDK 
•  ApplicaAon runAmes 
•  Java, Python 
•  Local development tools 
•  Eclipse plugin, AppEngine Launcher 
•  Specialized applicaAon services  
•  Cloud based dashboard 
•  Ready to scale  
•  Built in fault tolerance, load balancing 
Specialized Services
18 
Blobstore Images 
Mail  XMPP  Task Queue 
Memcache  Datastore  URL Fetch 
User Service 
Language runtimes
19 
Duke, the Java mascot 
Copyright © Sun Microsystems Inc., all rights reserved. 
Ensuring Portability
20 
Extended Language support
through JVM
•  Java
•  Scala
•  JRuby (Ruby)
•  Groovy
•  Quercus (PHP)
•  Rhino (JavaScript)
•  Jython (Python)
21 
Duke, the Java mascot 
Copyright © Sun Microsystems Inc., all rights reserved. 
Always free to get started
•  ~5M pageviews/month 
•  6.5 CPU hrs/day 
•  1 GB storage 
•  650K URL Fetch calls/day 
•  2,000 recipients emailed 
•  1 GB/day bandwidth 
•  100,000 tasks enqueued 
•  650K XMPP messages/day 
22
Application Platform Management
23 
App Engine Dashboard
24 
App Engine Health History
25 
Development Tools for App Engine
26 
Google App Engine Launcher 
27 
SDK Console
28 
Google Plugin for Eclipse 
29 
Two+ years in review 
30 
Apr 2008
 Python launch
May 2008
 Memcache, Images API
Jul 2008
 Logs export
Aug 2008
 Batch write/delete
Oct 2008
 HTTPS support
Dec 2008
 Status dashboard, quota details
Feb 2009
 Billing, larger files
Apr 2009
 Java launch, DB import, cron support, SDC
May 2009
 Key-only queries
Jun 2009
 Task queues
Aug 2009
 Kindless queries
Sep 2009
 XMPP
Oct 2009
 Incoming email
Dec 2009
 Blobstore
Feb 2010
 Datastore cursors, Appstats
Mar 2010
 Read policies, IPv6
May 2010
 App Engine for Business
Introducing App Engine for Business 
31
Same scalable cloud pla3orm, but designed for the Enterprise 
App Engine for Business 
Google App Engine for Business 
Details 
•  Enterprise applicaAon management 
–  Centralized domain console (preview available today) 
•  Enterprise reliability and support 
–  99.9% Service Level Agreement 
–   Direct support 
•  Hosted SQL 
–  RelaAonal SQL database in the cloud (preview available today) 
•  SSL on your domain 
•  Secure by default 
–  Integrated Single Sign On (SSO) 
•  Pricing that makes sense 
–  Apps cost $8 per user, up to $1000 max per month 
32 
Google App Engine
for Business
Enterprise App Development with Google 
33 
Build your own
Google App Engine
for Business
Buy from others
Google Apps
Marketplace
Enterprise Firewall 
Enterprise Data  AuthenAcaAon  Enterprise Services  User Management 
Buy from Google
Google Apps
for Business
Enterprise Application Platform
34 
App Engine for Business
Roadmap
Enterprise Administration
Console
Preview (signups available)
Direct Support Preview (signups available)
Hosted SQL Preview (signups available)
Service Level Agreement Available Q4 2010 (Draft published)
Enterprise billing Available Q4 2010
Custom Domain SSL Limited Release EOY 2010
App Engine Resources
Get started with App Engine 
•  hp://code.google.com/appengine 
Read up on App Engine for Business and become a trusted tester 
•  hp://code.google.com/appengine/business 
•  bit.ly/gae4b <‐ sign up! 
Part II - Google’s new Cloud Technologies
Topics covered
•  Google Storage for Developers
•  Prediction API (machine learning)
•  BigQuery
Google Storage for Developers
Store your data in Google's cloud
What Is Google Storage?
•  Store your data in Google's cloud 
o  any format, any amount, any Ame 
•  You control access to your data 
o  private, shared, or public 
•   Access via Google APIs or 3rd party tools/libraries 
Sample Use Cases
StaAc content hosAng 
e.g. staAc html, images, music, video 
Backup and recovery 
e.g. personal data, business records 
Sharing 
e.g. share data with your customers 
Data storage for applicaAons 
e.g. used as storage backend for Android, AppEngine, Cloud based apps 
Storage for ComputaAon 
e.g. BigQuery, PredicAon API 
Google Storage Benefits
High Performance and Scalability        
Backed by Google infrastructure  
Strong Security and Privacy 
        Control access to your data 
Easy to Use 
Get started fast with Google & 3rd party tools 
Google Storage Technical Details
•  RESTful API  
o  Verbs: GET, PUT, POST, HEAD, DELETE  
o  Resources: identified by URI 
o  Compatible with S3  
•  Buckets  
o  Flat containers  
•  Objects  
o  Any type 
o  Size: 100 GB / object 
•  Access Control for Google Accounts  
o  For individuals and groups  
•  Two Ways to Authenticate Requests  
o  Sign request using access keys  
o  Web browser login
Performance and Scalability
•  Objects of any type and 100 GB / Object 
•  Unlimited numbers of objects, 1000s of buckets 
•  All data replicated to mulAple US data centers 
•  Leveraging Google's worldwide network for data delivery  
•  Only you can use bucket names with your domain names  
•  Read‐your‐writes data consistency 
•  Range Get  
Security and Privacy Features
•  Key‐based authenAcaAon 
•  AuthenAcated downloads from a web browser 
•  Sharing with individuals 
•  Group sharing via Google Groups  
•  Access control for buckets and objects 
•  Set Read/Write/List permissions   
Demo
•  Tools: 
o  GS Manager 
o  GSUAl 
•  Upload / Download 
Google Storage usage within Google
HaiA Relief Imagery  USPTO data 
Partner ReporAng 
Google BigQuery  Google  
Predic/on API 
Partner ReporAng 
Some Early Google Storage Adopters 
Google Storage - Pricing
o  Storage 
 $0.17/GB/Month 
o  Network 
 Upload - $0.10/GB 
 Download 
 $0.15/GB Americas / EMEA 
 $0.30/GB  APAC 
o  Requests 
 PUT, POST, LIST - $0.01 / 1000 Requests 
 GET, HEAD - $0.01 / 10000 Requests
Google Storage - Availability
•  Limited preview in US currently  
o  100GB free storage and network from Google per account 
o  Sign up for waitlist at hp://code.google.com/apis/storage/ 
•  Note: Non US preview available on case‐by‐case basis 
Google Storage Summary
•  Store any kind of data using Google's cloud infrastructure 
•  Easy to Use APIs 
•  Many available tools and libraries 
o  gsuAl, GS Manager 
o  3rd party: 
 Boto, CloudBerry, CyberDuck, JetS3t, and more 
Google Prediction API
Google's prediction engine in the cloud
Introducing the Google Prediction API
•  Google's sophisticated machine learning technology
•  Available as an on-demand RESTful HTTP web service
How does it work? 
"english"  The quick brown fox jumped over the lazy 
dog. 
"english"  To err is human, but to really foul things up 
you need a computer. 
"spanish"  No hay mal que por bien no venga. 
"spanish"  La tercera es la vencida. 
?  To be or not to be, that is the quesAon. 
?  La fe mueve montañas. 
The Prediction API
finds relevant
features in the
sample data during
training.
The PredicAon API 
later searches for 
those features 
during predicAon. 
A virtually endless number of applicaAons... 
Customer
Sentiment
TransacAon 
Risk 
Species 
IdenAficaAon 
Message 
RouAng 
Legal Docket 
ClassificaAon 
Suspicious 
AcAvity 
Work Roster 
Assignment 
Recommend 
Products 
PoliAcal 
Bias 
UpliS 
MarkeAng 
Email 
Filtering 
DiagnosAcs 
Inappropriate 
Content 
Career 
Counselling 
Churn 
PredicAon 
... and many more ... 
A PredicAon API Example 
AutomaAcally categorize and respond to emails by language 
•  Customer: ACME Corp, a multinational organization
•  Goal: Respond to customer emails in their language
•  Data: Many emails, tagged with their languages
•  Outcome: Predict language and respond accordingly
Using the Prediction API
1. Upload 
2. Train 
Upload your training data to 
Google Storage  
Build a model from your data 
Make new predicAons 3. Predict 
A simple three step process... 
Step 1: Upload 
Upload your training data to Google Storage 
•  Training data: outputs and input features
•  Data format: comma separated value format
(CSV)
"english","To err is human, but to really ..." 
"spanish","No hay mal que por bien no venga." 
...
Upload to Google Storage 
gsutil cp ${data} gs://yourbucket/${data}
Step 2: Train 
Create a new model by training on data 
To train a model:
POST prediction/v1.1/training?data=mybucket%2Fmydata
Training runs asynchronously. To see if it has
finished:
GET prediction/v1.1/training/mybucket
%2Fmydata
{"data":{
"data":"mybucket/mydata",
"modelinfo":"estimated accuracy: 0.xx"}}}
Step 3: Predict 
Apply the trained model to make predicAons on new data 
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}
Step 3: Predict 
Apply the trained model to make predicAons on new data 
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}
{ data : {
"kind" : "prediction#output",
"outputLabel":"French",
"outputMulti" :[
{"label":"French", "score": x.xx}
{"label":"English", "score": x.xx}
{"label":"Spanish", "score": x.xx}]}}
Step 3: Predict 
Apply the trained model to make predicAons on new data 
import httplib
header = {"Content-Type" : "application/json"}
#...put new data in JSON format in params variable
conn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST",
"/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header)
print conn.getresponse()
An example using Python 
PredicAon API CapabiliAes 
Data
•  Input Features: numeric or unstructured text
•  Output: up to hundreds of discrete categories
Training
•  Many machine learning techniques
•  Automatically selected
•  Performed asynchronously
Access from many platforms:
•  Web app from Google App Engine
•  Apps Script (e.g. from Google Spreadsheet)
•  Desktop app
PredicAon API v1.1  ‐ new features 
•  Updated Syntax
•  Multi-category prediction
o  Tag entry with multiple labels
•  Continuous Output
o  Finer grained prediction rankings based on multiple labels
•  Mixed Inputs
o  Both numeric and text inputs are now supported
Can combine continuous output with mixed
inputs
Google BigQuery
Interactive analysis of large datasets in Google's cloud
Introducing Google BigQuery
•  Google's large data adhoc analysis technology
o  Analyze massive amounts of data in seconds
•  Simple SQL-like query language
•  Flexible access
o  REST APIs, JSON-RPC, Google Apps Script
Why BigQuery?  
Working with large data is a challenge 
Many Use Cases ... 
Spam
Trends 
DetecAon 
Web Dashboards  Network 
OpAmizaAon 
InteracAve Tools 
Key CapabiliAes of BigQuery 
•  Scalable: Billions of rows
•  Fast: Response in seconds
•  Simple: Queries in SQL
•  Web Service
o  REST
o  JSON-RPC
o  Google App Scripts
Using BigQuery
1. Upload 
2. Import 
Upload your raw data to 
Google Storage  
Import raw data into BigQuery table 
Perform SQL queries on table 3. Query 
Another simple three step process... 
WriAng Queries 
Compact subset of SQL
o  SELECT ... FROM ...
WHERE ...
GROUP BY ... ORDER BY ...
LIMIT ...;
Common functions
o  Math, String, Time, ...
Statistical approximations
o  TOP
o  COUNT DISTINCT
BigQuery via REST 
GET /bigquery/v1/tables/{table name}
GET /bigquery/v1/query?q={query}
Sample JSON Reply:
{
"results": {
"fields": { [
{"id":"COUNT(*)","type":"uint64"}, ... ]
},
"rows": [
{"f":[{"v":"2949"}, ...]},
{"f":[{"v":"5387"}, ...]}, ... ]
}
}
Also supports JSON-RPC
Security and Privacy 
Standard Google Authentication
•  Client Login
•  OAuth
•  AuthSub
HTTPS support
•  protects your credentials
•  protects your data
Relies on Google Storage to manage
access
Large Data Analysis Example 
Wikimedia Revision history data from: hp://download.wikimedia.org/enwiki/latest/enwiki‐
latest‐pages‐meta‐history.xml.7z 
Wikimedia Revision History 
Using BigQuery Shell 
Python DB API 2.0 + B. Clapper's sqlcmd
http://www.clapper.org/software/python/sqlcmd/
BigQuery from a Spreadsheet 
BigQuery from a Spreadsheet 
Recap 
•  Google Storage
o  High speed data storage on Google Cloud
•  Prediction API
o  Google's machine learning technology able to
predict outcomes based on sample data
•  BigQuery
o  Interactive analysis of very large data sets
o  Simple SQL query language access
Further info available at: 
•  Google Storage for Developers
o  http://code.google.com/apis/storage
•  Prediction API
o  http://code.google.com/apis/predict
•  BigQuery
o  http://code.google.com/apis/bigquery
Q&A
Thank You! 
Chris Schalk 
Google Developer 
Advocate 
hp://twier.com/cschalk 

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