Building Great Companies on the Cloud


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Building Great Companies on the Cloud

  1. 1. Building Great Companies on the Cloud Roman Stanek Founder, CEO Good Data
  2. 2. Promise This presentation contains no “every cloud has silver lining” joke :-|
  3. 3. Who am I Roman - quot;Stanquot; at Starbuck's Technologist Entrepreneur (NetBeans, Systinet) Blogger ( Czech
  4. 4. What is happening to computing today is a revolution, the biggest upheaval since the invention of the PC in the 1970s. Nicholas Carr
  5. 5. quot;It's stupidity. It's worse than stupidity: it's a marketing hype campaign Richard Stallman Founder, Free Software Foundation
  6. 6. But we all struggle with the cloud definition...
  7. 7. Most of all the graphics artists...
  8. 8. 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
  9. 9. No, you cannot install cloud on your notebook.
  10. 10. Security & control
  11. 11. 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.
  12. 12. 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.
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. Amazon Security/ SLAs Multiple redundant sites SAS70 Amazon EC2 SLA - 99.95% 4 hrs, 22 min/year Amazon S3 SLA - 99.9%
  19. 19. Control: Plenty of startups solving this problem already
  20. 20. Cloud or No Cloud? For SMBs, data is safer in the cloud Secure, auditable, fully compliant Pick your cloud provider carefully
  21. 21. Technology
  22. 22. Acronyms don’t matter: Caches, bloom filters, bitmap indexes, column stores, distributed key/value stores and document databases
  23. 23. MapReduce law: If It Can Be Done in Parallel, It Will Be
  24. 24. ACID -> BASE Traditional approaches don’t scale BASE - basically available, soft state, eventually consistent: BigTable, SimpleDB, Cassandra, Dynamo
  25. 25. 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)
  26. 26. TechCrunch Effect
  27. 27. 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
  28. 28. Cloud APIs True SOA Loosely coupled - REST, Atom Encapsulate cloud services: Control APIs Data APIs Application Functionality APIs
  29. 29. Open Cloud Manifesto Prevent vendor lock-in “or” Limit innovation
  30. 30. Cloud economics
  31. 31. If you want to change the game, change the economics of how the game is played Alan M. Webber
  32. 32. Startups in the cloud Infrastructure labor savings No CAPEX: Less equity goes to VCs Unpredictable demand (up and down)
  33. 33. 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
  34. 34. IT vs. Clouds Losing their monopoly on the infrastructure It’s all about economics
  35. 35. 1 email message $0.0001
  36. 36. Fixed pricing Most widely used pricing No supply/demand Simple, predictable AWS, Google App Engine: CPU, Storage, network traffic
  37. 37. 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”
  38. 38. Cloud providers Economies of scale Utilization and efficiency SLAs Question: Long-term viability?
  39. 39. 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?
  40. 40. 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)
  41. 41. 5 Public clouds + 500 Private clouds 505 Clouds in 2015
  42. 42. Business impact
  43. 43. Winners Google, Cisco SaaS vendors
  44. 44. Losers Big server vendors (HP, Sun, Dell) Monolithic app providers Microsoft
  45. 45. Real winners Innovation SMBs, Startups The little guy wins
  46. 46. Good Data: On Demand Business Intelligence Cloud + Web 2.0 metaphors Flickr for Data Simplicity & Collaboration
  47. 47. Company status 30 employees Development in Prague Sales & marketing in San Francisco Funded by industry luminaries
  48. 48. Thank you!
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