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Building Great
  Companies
 on the Cloud

    Roman Stanek
Founder, CEO Good Data
Promise
   This presentation
  contains no “every
cloud has silver lining”
          joke
           :-|
Who am I
Roman - quot;Stanquot; at
Starbuck's

Technologist

Entrepreneur (NetBeans,
Systinet)

Blogger (roman.stanek.org)

Czech
What is happening to
computing today is a
revolution, the biggest
upheaval since the
invention of the PC
in the 1970s.


Nicholas Carr
quot;It's stupidity.
It's worse than
stupidity: it's a
marketing
hype campaign

  Richard Stallman
Founder, Free Software
     Foundation
But we all
struggle with the
cloud definition...
Most of all the
graphics artists...
Definition:
Clouds are hardware-based services
offering compute,   network and
storage capacity where:

Hardware management is highly
abstracted from the buyer
Buyers incur infrastructure costs as
variable OPEX
Infrastructure capacity is highly
elastic (up or down)
       McKinsey & Company
No, you
cannot
install
cloud on
your
notebook.
Security & control
By a 5‐to‐1 ratio,
 companies trust internal
    IT systems over cloud‐
 based technologies due to
        fear about security
threats and loss of control
      of data and systems.
                   Avanade Inc.
They are right...
Streamload: On June 15,
2007 system
administrator's script
accidentally misidentified
and deleted quot;good dataquot;
along with the quot;dead dataquot; of
some 3.5 million former
user accounts and files.
Or are they?
LAX Los Angeles                                                                                                  1,200


     MIA Miami                                                                                         1,000


  JFK New York                                                                                   900


  ORD Chicago                                                                              825


  EWR Newark                                                                        750


                  0                        300                      600                      900               1200


10 U.S. airports with the highest weekly frequency of laptop loss
                      Airport Insecurity: The Case of Missing & Lost Laptops, Ponemon Institute LLC
This get scary...
 Did not protect sensitive
                                                                                      65%
 information contained on laptop


                                                                                57%
 Worry about losing their laptop

Laptop contains
                                                                           53%
confidential company information


                                                                  42%
 Data on laptop is not backed up

                                   0        0.175         0.35          0.525          0.7



      Airport Insecurity: The Case of Missing & Lost Laptops, Ponemon Institute LLC
Most likely causes of data
               breach?

Negligent insiders                                                                       75%

Outsourced data                                                 42%

Malicious insiders                              26%

Social engineering         2%

         Hackers           1%

                       0                0.2               0.4                0.6          0.8


          2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
IT environment where data
          breaches occur
Off-network devices                                                                       58%

         Networks                                                                   50%

        Mainframes                                                       41%

         Paper files                                                   39%

           Backups                           18%

                       0              0.15               0.3               0.45           0.6




          2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
Ability to detect the loss or
   theft of confidential
        information
                                                       31%

                                       25%

                                                                      18%
                        16%

        10%




                                   Somehow
  Very confident Confident         confident Not confident          Unsure

 2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
Amazon Security/
       SLAs
Multiple redundant sites
SAS70


Amazon EC2 SLA - 99.95%
  4 hrs, 22 min/year

Amazon S3 SLA - 99.9%
Control: Plenty of startups
solving this problem already
Cloud or No Cloud?

For SMBs, data is safer
in the cloud
Secure, auditable, fully
compliant
Pick your cloud provider
carefully
Technology
Acronyms don’t
      matter:

Caches, bloom filters, bitmap
   indexes, column stores,
distributed key/value stores
  and document databases
MapReduce law:

If It Can Be Done in
 Parallel, It Will Be
ACID -> BASE
Traditional approaches don’t
scale
BASE - basically available, soft
state, eventually consistent:
  BigTable, SimpleDB,
  Cassandra, Dynamo
GoodData: Innovate
   vs. leverage?
Processing Power
   Cloud makes ROLAP approach possible
Elastic Scale
   IT builds for peak load, we don’t have to
Multi-Tenancy
   Single instance across 1000s of customers
Stateless
   Massive load balancing (shared nothing)
TechCrunch Effect
Public cloud classes

            AWS         MS Azure     Google AE

                                     Predefined
 CPU         x86          .Net
                                     framework
           EBS, S3,     SQL, Azure
Storage   SimpleDB        store
                                      BigTable


Network   Declarative   Automatic      Fixed
Cloud APIs

True SOA
Loosely coupled - REST, Atom
Encapsulate cloud services:
  Control APIs
  Data APIs
  Application Functionality APIs
Open Cloud
Manifesto
Prevent vendor lock-in
          “or”
   Limit innovation
Cloud economics
If you want to
change the
game, change
the economics
of how the
game is
played

Alan M. Webber
Startups in the cloud
 Infrastructure labor savings
 No CAPEX: Less equity goes to VCs
 Unpredictable demand (up and down)
Succeed (or fail)
       faster
$500k to start technology company
Big bets aren't as big anymore
Easier for startups to adapt to shifts
Level playing field for startups
IT vs. Clouds
Losing their monopoly on the
infrastructure
It’s all about economics
1 email
message

$0.0001
Fixed pricing
Most widely used pricing
No supply/demand
Simple, predictable
AWS, Google App Engine:
   CPU, Storage, network traffic
Variable pricing
Reserved instance price (AWS):
“I have 10 instances running 24x7”
Spot price/future price:
“I want 1,000 instances at the end of
the quarter”
Off-peak pricing:
“Run my MapReduce app 10 hours
every day”
Cloud providers
Economies of scale
Utilization and efficiency
SLAs


Question: Long-term viability?
Private clouds
Violate #2 of our cloud definition:
  Buyers incur infrastructure costs
  as CAPEX
Virtualization on top of traditional
enterprise IT stack
Encapsulation of IT infrastructure
Scale?
Economies of scale
 Technology                   Medium-sized DC                            Very Large DC                                Ratio


                               $95 per Mbit/sec/                      $13 per Mbit/sec/
   Network                                                                                                              7.1
                                    month                                  month

                              $2.20 per GByte /                       $0.40 per GByte /
    Storage                                                                                                             5.7
                                   month                                   month

                                  140 Servers /                         >1000 Servers /
Administration                                                                                                          7.1
                                  Administrator                          Administrator




  HAMILTON, J. Internet-Scale Service Efficiency. In Large-Scale Distributed Systems and Middleware (LADIS) Workshop (September 2008)
5 Public clouds
+
500 Private clouds
505 Clouds in 2015
Business impact
Winners

Google, Amazon.com
Cisco
SaaS vendors
Losers
Big server vendors
(HP, Sun, Dell)
Monolithic app providers
Microsoft
Real winners

Innovation
SMBs, Startups
The little guy wins
Good Data: On Demand
  Business Intelligence

Cloud + Web 2.0
metaphors
Flickr for Data
Simplicity &
Collaboration
Company status
30 employees
Development in
Prague
Sales & marketing in
San Francisco
Funded by industry
luminaries
www.gooddata.com
Thank you!

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Building Great Companies on the Cloud

  • 1. Building Great Companies on the Cloud Roman Stanek Founder, CEO Good Data
  • 2. Promise This presentation contains no “every cloud has silver lining” joke :-|
  • 3. Who am I Roman - quot;Stanquot; at Starbuck's Technologist Entrepreneur (NetBeans, Systinet) Blogger (roman.stanek.org) Czech
  • 4.
  • 5.
  • 6. What is happening to computing today is a revolution, the biggest upheaval since the invention of the PC in the 1970s. Nicholas Carr
  • 7. quot;It's stupidity. It's worse than stupidity: it's a marketing hype campaign Richard Stallman Founder, Free Software Foundation
  • 8. But we all struggle with the cloud definition...
  • 9. Most of all the graphics artists...
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. Definition: Clouds are hardware-based services offering compute, network and storage capacity where: Hardware management is highly abstracted from the buyer Buyers incur infrastructure costs as variable OPEX Infrastructure capacity is highly elastic (up or down) McKinsey & Company
  • 15.
  • 18. By a 5‐to‐1 ratio, companies trust internal IT systems over cloud‐ based technologies due to fear about security threats and loss of control of data and systems. Avanade Inc.
  • 19. They are right... Streamload: On June 15, 2007 system administrator's script accidentally misidentified and deleted quot;good dataquot; along with the quot;dead dataquot; of some 3.5 million former user accounts and files.
  • 20. Or are they? LAX Los Angeles 1,200 MIA Miami 1,000 JFK New York 900 ORD Chicago 825 EWR Newark 750 0 300 600 900 1200 10 U.S. airports with the highest weekly frequency of laptop loss Airport Insecurity: The Case of Missing & Lost Laptops, Ponemon Institute LLC
  • 21. This get scary... Did not protect sensitive 65% information contained on laptop 57% Worry about losing their laptop Laptop contains 53% confidential company information 42% Data on laptop is not backed up 0 0.175 0.35 0.525 0.7 Airport Insecurity: The Case of Missing & Lost Laptops, Ponemon Institute LLC
  • 22. Most likely causes of data breach? Negligent insiders 75% Outsourced data 42% Malicious insiders 26% Social engineering 2% Hackers 1% 0 0.2 0.4 0.6 0.8 2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
  • 23. IT environment where data breaches occur Off-network devices 58% Networks 50% Mainframes 41% Paper files 39% Backups 18% 0 0.15 0.3 0.45 0.6 2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
  • 24. Ability to detect the loss or theft of confidential information 31% 25% 18% 16% 10% Somehow Very confident Confident confident Not confident Unsure 2008 Study on the Uncertainty of Data Breach Detection,Ponemon Institute LLC
  • 25. Amazon Security/ SLAs Multiple redundant sites SAS70 Amazon EC2 SLA - 99.95% 4 hrs, 22 min/year Amazon S3 SLA - 99.9%
  • 26.
  • 27. Control: Plenty of startups solving this problem already
  • 28. Cloud or No Cloud? For SMBs, data is safer in the cloud Secure, auditable, fully compliant Pick your cloud provider carefully
  • 30.
  • 31. Acronyms don’t matter: Caches, bloom filters, bitmap indexes, column stores, distributed key/value stores and document databases
  • 32. MapReduce law: If It Can Be Done in Parallel, It Will Be
  • 33. ACID -> BASE Traditional approaches don’t scale BASE - basically available, soft state, eventually consistent: BigTable, SimpleDB, Cassandra, Dynamo
  • 34. GoodData: Innovate vs. leverage? Processing Power Cloud makes ROLAP approach possible Elastic Scale IT builds for peak load, we don’t have to Multi-Tenancy Single instance across 1000s of customers Stateless Massive load balancing (shared nothing)
  • 36. Public cloud classes AWS MS Azure Google AE Predefined CPU x86 .Net framework EBS, S3, SQL, Azure Storage SimpleDB store BigTable Network Declarative Automatic Fixed
  • 37. Cloud APIs True SOA Loosely coupled - REST, Atom Encapsulate cloud services: Control APIs Data APIs Application Functionality APIs
  • 38. Open Cloud Manifesto Prevent vendor lock-in “or” Limit innovation
  • 40. If you want to change the game, change the economics of how the game is played Alan M. Webber
  • 41. Startups in the cloud Infrastructure labor savings No CAPEX: Less equity goes to VCs Unpredictable demand (up and down)
  • 42. Succeed (or fail) faster $500k to start technology company Big bets aren't as big anymore Easier for startups to adapt to shifts Level playing field for startups
  • 43. IT vs. Clouds Losing their monopoly on the infrastructure It’s all about economics
  • 45. Fixed pricing Most widely used pricing No supply/demand Simple, predictable AWS, Google App Engine: CPU, Storage, network traffic
  • 46. Variable pricing Reserved instance price (AWS): “I have 10 instances running 24x7” Spot price/future price: “I want 1,000 instances at the end of the quarter” Off-peak pricing: “Run my MapReduce app 10 hours every day”
  • 47. Cloud providers Economies of scale Utilization and efficiency SLAs Question: Long-term viability?
  • 48.
  • 49. Private clouds Violate #2 of our cloud definition: Buyers incur infrastructure costs as CAPEX Virtualization on top of traditional enterprise IT stack Encapsulation of IT infrastructure Scale?
  • 50. Economies of scale Technology Medium-sized DC Very Large DC Ratio $95 per Mbit/sec/ $13 per Mbit/sec/ Network 7.1 month month $2.20 per GByte / $0.40 per GByte / Storage 5.7 month month 140 Servers / >1000 Servers / Administration 7.1 Administrator Administrator HAMILTON, J. Internet-Scale Service Efficiency. In Large-Scale Distributed Systems and Middleware (LADIS) Workshop (September 2008)
  • 51. 5 Public clouds + 500 Private clouds 505 Clouds in 2015
  • 54. Losers Big server vendors (HP, Sun, Dell) Monolithic app providers Microsoft
  • 56. Good Data: On Demand Business Intelligence Cloud + Web 2.0 metaphors Flickr for Data Simplicity & Collaboration
  • 57. Company status 30 employees Development in Prague Sales & marketing in San Francisco Funded by industry luminaries www.gooddata.com