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Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
Particle Collision in Code Space. API meets Big Data
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Particle Collision in Code Space. API meets Big Data

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Presentation by Chris Boos (CEO arago AG) at the "How to Web" in Bukarest on 11/08/12

Presentation by Chris Boos (CEO arago AG) at the "How to Web" in Bukarest on 11/08/12

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  • Transcript

    • 1. Particle Collision In Code Space API meets Big Data Chris Boos (@boosc) boos@arago.de How to Web, Bucarest 8.11.2012Mittwoch, 14. November 12
    • 2. Big Data?Mittwoch, 14. November 12
    • 3. Data, lots of itMittwoch, 14. November 12
    • 4. Even in simple datasets, common statistics fails - (avg, min, max, distribution)Mittwoch, 14. November 12
    • 5. Why you need big data You Are Here ! Yield 2010s Systems Thinking Wisdome 2000s Knowledge Ecology Intelligence 1990s Knowledge Management Knowledge 1980s 1970s Information Management Information 1960s 1950s Data Processing DataMittwoch, 14. November 12
    • 6. Because, (now) We Can!Mittwoch, 14. November 12
    • 7. 79 times more CPU power than used in Apollo missions on one iPhone 4Mittwoch, 14. November 12
    • 8. All the resources you like at your fingertipsMittwoch, 14. November 12
    • 9. Buzzword Bingo H-Space Agents/Bots Data Engineer Machine Learning Support Vector Machines Big Data Swarm Intelligence Gaussian Processes Genetic Algorithms Hadoop PIG HBase Cassandra redis.io Eucalyptus Core Dataset R+ Clustering NoStats Natural Language ProcessingMittwoch, 14. November 12
    • 10. Data Exploration Is a Big AdventureMittwoch, 14. November 12
    • 11. What people think Big Data is all about improving prognostic power - guess what it is NOT!Mittwoch, 14. November 12
    • 12. Warming up Finding clusters, evaluating outliers and interpreting white noiseMittwoch, 14. November 12
    • 13. You are not looking for patterns, you are looking for anomaliesMittwoch, 14. November 12
    • 14. Two tips for looking at data: 1. Plot it 2. Remove all labelsMittwoch, 14. November 12
    • 15. 3 Ways to solve a coding or data problem Elegant Standard Brute ForceMittwoch, 14. November 12
    • 16. The Sledge Hammer of Big Data - Map ReduceMittwoch, 14. November 12
    • 17. Old Style (Imperative) Programming • Step by step explanation 1 what to do • Explaining WHAT to do rather than RESULTS you want 2 • Always necessary for basic algorithms 3Mittwoch, 14. November 12
    • 18. One New Style (Functional) Programming I • Combine results to 1 become a program 2 • Allows dynamic 3 distribution • Map-Reduce is only one way of doing it!Mittwoch, 14. November 12
    • 19. Functional Programming II F ( G ( H ( A,B) , C), D) getMusicLikes(getFriends(facebookID) Instead of for i in getFriends(facebookID) getMusicLikes(i)Mittwoch, 14. November 12
    • 20. BASE (Basically Available, Soft State, Eventual consistency) not ACID (Atomicity, Consistency, Isolation, Durability)Mittwoch, 14. November 12
    • 21. That is all batch processing!?Mittwoch, 14. November 12
    • 22. The big in memory trend is delaying the problemMittwoch, 14. November 12
    • 23. Google is going well beyond Map Reduce and batch processingMittwoch, 14. November 12
    • 24. And at facebook they are going far beyond distributed data storage and processingMittwoch, 14. November 12
    • 25. Most people using Big Data end up building their own platformMittwoch, 14. November 12
    • 26. APIs, for external use only? NO WAY!Mittwoch, 14. November 12
    • 27. Monolithic architectures are so out!!!Mittwoch, 14. November 12
    • 28. Do not use Design Patterns as an excuse to get lost in OO space!!Mittwoch, 14. November 12
    • 29. Standardising dev tools can mean 2 things: You are a little dictator and could not find a country Your team is not as good as you say and needs guidanceMittwoch, 14. November 12
    • 30. API 1.0Mittwoch, 14. November 12
    • 31. Function calls or servicesMittwoch, 14. November 12
    • 32. Stop religion on transfer format (XML, JSON), who cares?Mittwoch, 14. November 12
    • 33. Stop religion on service design (SOAP, REST), be practical!Mittwoch, 14. November 12
    • 34. API 2.0Mittwoch, 14. November 12
    • 35. Adding semantics means adding contextMittwoch, 14. November 12
    • 36. OWL or the theoretic vision of everyone speaking the same language and meaning the same thingMittwoch, 14. November 12
    • 37. Facebook‘s pragmatic approach: the open graph, sort of RDFMittwoch, 14. November 12
    • 38. API Management and MonetizationMittwoch, 14. November 12
    • 39. oAuth vs. application tokens - or both?Mittwoch, 14. November 12
    • 40. Monetization models API calls Data transfer Data accessMittwoch, 14. November 12
    • 41. Not necessarily DIY, why not use an external API manager?Mittwoch, 14. November 12
    • 42. Collision BIG Data and APIsMittwoch, 14. November 12
    • 43. Your platform cannot handle infinite explorationMittwoch, 14. November 12
    • 44. Your users will not understand the mechanics behind your data processingMittwoch, 14. November 12
    • 45. Your data has to be put into contextMittwoch, 14. November 12
    • 46. Then you can onboard new team members quickly and get external developers to use your stuffMittwoch, 14. November 12
    • 47. Check out my tool list: http://www.hcboos.net/100-links/Mittwoch, 14. November 12
    • 48. Credits • Images on pages 8,11,15,21,22,25,29,31,32,33,34,39,40,41,43,44,45,46 are used with kind permission of CERN, are (c) CERN and are used under the following terms of use: http://copyright.web.cern.ch/ • „Big Data Just Beginning to Explode“ by CSC http://www.csc.com/insights/flxwd/78931- big_data_just_beginning_to_explode • „Social media network connections among twitter users“ by Marc Smith http://www.flickr.com/photos/ marc_smith/ • Asteroid Datasets by Bruce Gary http://brucegary.net/POVENMIRE/x.htm • iPhone Images (c) Apple Computer inc. • Google Percolator chart by Marc Bojoly http://blog.octo.com/en/my-reading-of-percolator-architecture-a- google-search-engine-component/ • Prism image by Deviant Art http://sudden2.deviantart.com/art/Floyd-s-Prism-51863247 • Monolith image found on http://www.themonolith.com/ • Design Patterns: Elements of Reusable Object Oriented Software, Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1994, Addison Wesley, ISBN 0-201-63361-2 • OWL Logo (c) World Wide Web Consortium http://www.w3c.orgMittwoch, 14. November 12
    • 49. Thank You for Your Time Chris Boos (@boosc) boos@arago.de How to Web, Bucarest 8.11.2012Mittwoch, 14. November 12
    • 50. 2 ExamplesMittwoch, 14. November 12
    • 51. The AMP3 Platform at Senzari.com Adaptable Music Parallel Processing PlatformMittwoch, 14. November 12
    • 52. MARS-o-Matic at arago.de Big Data based IT modelling and pricing app More Info at www.mars-o-matic.comMittwoch, 14. November 12

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