Strata Conference NYC 2013 Full Version

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Strata Conference NYC 2013 Full Version

  1. 1. Taewook Eom Data Infrastructure Team SK planet 2014-01-28
  2. 2. Taewook Eom Data Programmer Plaster(Planet Master) of Big Data Infra Pre-Assessor of Hiring Programmers Mentor of 101 Startup Korea Twitter: @taewooke LinkedIn: http://kr.linkedin.com/in/taewookeom http://www.flickr.com/photos/oreillyconf/10616622085/
  3. 3. Santa Clara : Technical New York with Cloudera : Financial, Business Europe : Privacy, Government Boston : Medical http://strataconf.com/ by O’Reilly Web 2.0 : Open, Sharing, Participation Big Data : Making Data Work Change the World with Data.
  4. 4. Data When hardware became commoditized, software was valuable. Now software being commoditized, data is valuable. – Tim O’Reilly, 2011 Data is like the blood of the enterprise. – Amr Awadallah, CTO at Cloudera, 2013
  5. 5. What is Big Data? All data that is not a fit for a traditional RDBMS, whether used for OLTP or Analytics purposes Big Data Architectural Patterns http://strataconf.com/stratany2013/public/schedule/detail/30397
  6. 6. Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data - Gartner, 2011 http://blog.vitria.com/Portals/47881/images/3values-resized-600.png
  7. 7. http://image-store.slidesharecdn.com/ae63030a-3d9b-11e3-9cff-22000a970267-original.jpg
  8. 8. Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS http://strataconf.com/stratany2013/public/schedule/detail/29968
  9. 9. Data Science http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram http://en.wikipedia.org/wiki/File:DataScienceDisciplines.png
  10. 10. Big Data http://mappingignorance.org/fx/media/2013/07/Figura-11.jpg Open Mind!
  11. 11. Big Data Gartner's 2013 Hype Cycle for Emerging Technologies (2013-08-19)
  12. 12. more than half of technical sessions are presented by Chinese or Indian 39 of 125 sessions are sponsored sessions
  13. 13. Big Data: 4 Approaches Hadoop-based RDB-based Search-based NoSQL
  14. 14. Real-time Processing Real-time Recommendations for Retail: Architecture, Algorithms, and Design http://strataconf.com/stratany2013/public/schedule/detail/30217
  15. 15. Real-time Stream Processing Apache Kafka Gathering Apache Storm Processing Querying Streaming Search-based NoSQL SQL Stringer/Tez Shark
  16. 16. … not yet Graph Processing
  17. 17. Big Data Space No one tools is the right fit for all Big Data problem Do not be afraid to recommend the right solution for the problem over the popular solution To do this, you must be aware of the entire ecosystem Big Data Architectural Patterns http://strataconf.com/stratany2013/public/schedule/detail/30397
  18. 18. Practical Performance Analysis and Tuning for Cloudera Impala http://strataconf.com/stratany2013/public/schedule/detail/30551
  19. 19. Big Data Architectural Patterns http://strataconf.com/stratany2013/public/schedule/detail/30397
  20. 20. Hadoop and the Relational Data Warehouse – When to Use Which? http://strataconf.com/stratany2013/public/schedule/detail/30964
  21. 21. Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS http://strataconf.com/stratany2013/public/schedule/detail/29968
  22. 22. Each speaker is allocated five minutes of presentation time and is accompanied by 20 presentation slides. During presentations, each slide is displayed for 15 seconds and then automatically advanced. - http://en.wikipedia.org/wiki/Ignite_(event) http://oreilly.com/pub/pr/2242
  23. 23. Ignite Talks Hilary: The Most Poisoned Name In US History - Hilary Parker sudo make me a visualization! - Jeroen Janssens Design as a Fulcrum for Societal Change: the influence of Jimmyjane on female sexuality - Lisa Green Spaces in Between: The Transdisciplinary Niche to Type 1 Diabetes Living - Jorge Luna Why are women better data scientists than men? - Carolyn Martin Memoirs of a Prolific Moonlighter: A Chronic Writing Disorder…or Insanity? - Matthew Russell The Data Behind H1B Visas - Melissa Smolensky Signal Detection Theory: Man vs Machine - Kyle Redinger Algorithms of Pain - Heather Fenby Hadoop Playlist - Adam Kawa Why a Data Community is like a Music Scene - Harlan Harris A Tale of two Kinds of Startups - Jen van der Meer http://strataconf.com/stratany2013/public/schedule/detail/32182
  24. 24. Ignite Signal Detection Theory: Man vs Machine Co-Founder @VividCortex Kyle Redinger http://www.youtube.com/watch?v=Fg6mN-jevds (5 minutes 6 seconds) http://www.slideshare.net/realkyleredinger/man-vs-machine-signal-detection-theory-and-big-data
  25. 25. Signal Detection Theory: Man vs Machine Remove the obvious and look at what is important Remember: Less is more.
  26. 26. Ignite A Tale of Two Kinds of Startups CSO at Luminary Labs Jen van der Meer http://www.youtube.com/watch?v=0ooIs4cy5uM (5 minutes 2 seconds) http://www.slideshare.net/bettybluegreen/twokindsofstartups
  27. 27. Keynote Towards Strata 2014 Director of market research at O’Reilly Media Roger Magoulas http://www.youtube.com/watch?v=Ytd5VkEgQf8 (5 minutes 26 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31935 http://www.oreilly.com/data/free/files/stratasurvey.pdf
  28. 28. Towards Strata 2014
  29. 29. Towards Strata 2014
  30. 30. Towards Strata 2014
  31. 31. Towards Strata 2014
  32. 32. Science is fundamentally about data, but data is not fundamentally about science Beyond R and Ph.D.s: The Mythology of Data Science Debunked Douglas Merrill (ZestFinance) http://www.youtube.com/watch?v=J2sgObXbIWY (8 minutes 9 seconds)
  33. 33. People A data scientist is a data analyst who lives in California. – George Roumeliotis, (Intuit)
  34. 34. http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html
  35. 35. Data Data Data Data Businessperson: Business person, Leader, Entrepreneur Creative: Artist, Jack-of-All-Trades, Hacker Researcher: Scientist, Researcher, Statistician Engineer: Engineer, Developer http://datacommunitydc.org/blog/2012/08/data-scientists-survey-results-teaser/ http://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf
  36. 36. Scientists think they can code, software engineers think they are scientists. Team them up so they collaborate. – Scott Sorenson (Ancestry.com) Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop
  37. 37. How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce http://strataconf.com/stratany2013/public/schedule/detail/30707
  38. 38. Data scientists spend their lives as data janitors instead of leveraging their skills – Wes McKinney (DataPad) Building More Productive Data Science and Analytics Workflows
  39. 39. Keynote Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data Professor at the NYU Stern School of Business Foster Provost http://www.youtube.com/watch?v=1jzMiAfLH2c (10 minutes 16 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31685
  40. 40. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  41. 41. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  42. 42. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data Predictive does not mean actionable. – Scott Sorenson (Ancestry.com) Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop
  43. 43. More data gives you more precision, not more prediction. Using multiple datasets to reduce errors when measuring values. Is Bigger Really Better? - Ravi Iyer (Ranker.com) Predictive Analytics with Fine-grained Understand yourData Users, and Employees Behavior Customers, Using Graphs of Data to
  44. 44. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  45. 45. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  46. 46. Keynote Big Impact from Big Data Head of Analytics at Facebook Ken Rudin http://www.youtube.com/watch?v=RJFwsZwTBgg (11 minutes 57 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31903
  47. 47. Big Impact from Big Data
  48. 48. Hadoop is a hammer, but you need other tools along with it. Designing Your Data-Centric Organization Josh Klahr (Pivotal) http://www.youtube.com/watch?v=D86udfrVzrI (12 minutes)
  49. 49. Big Impact from Big Data The way you organize information depends on the question you intend to ask of it. - Richard Saul Wurman Building a Data Platform
  50. 50. HaDump : Loading data into Hadoop for not reason. Data Science Without a Scientist http://strataconf.com/stratany2013/public/schedule/detail/31801
  51. 51. Big Impact from Big Data Technical people still don't understand the business needs of business people! Business people don't know what's a table. - Anurag Tandon (MicroStrategy) Inject Big Data into your Corporate DNA: Enable Every Employee to Make Data Driven Decisions
  52. 52. Ask the Right Questions Organizations already have people who know their own data better than mystical data scientists. Learning Hadoop is easier than learning the company’s business. - Gartner, 2012 Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS http://strataconf.com/stratany2013/public/schedule/detail/29968
  53. 53. Non-linear Storytelling: Towards New Methods and Aesthetics for Data Narrative http://strataconf.com/stratany2013/public/schedule/detail/30207
  54. 54. Every Soldier is a Sensor: Countering Corruption in Afghanistan http://strataconf.com/stratany2013/public/schedule/detail/30828
  55. 55. Big Impact from Big Data
  56. 56. Big Impact from Big Data
  57. 57. Big Impact from Big Data
  58. 58. Value of Data Usable < Useful < Actionable with Impact If you can't answer for "so what?", you only have facts, not insight - Baron Schwartz (VividCortex Inc) Making Big Data Small Descriptive (Easy) Predictive (Medium) Prescriptive (Hard) What happened? What will happen? What should we do about it? Hadoop & Data Science for the Enterprise
  59. 59. The Future of Hadoop : What Happened & What's Possible? Co-Founder of Hadoop Doug Cutting http://www.youtube.com/watch?v=_WwuZI6AhN8 (14 minutes 41 seconds) http://strataconf.com/stratany2013/public/ schedule/detail/31591 Big Data is first industry that was created by open source. - Jack Norris (MapR Technologies) Separating Hadoop Myths from Reality Hadoop the kernel of the OS for data.
  60. 60. Hadoop's Impact on the Future of Data Management Mike Olson (Cloudera) http://www.youtube.com/watch?v=puHS2JNKgRM http://strataconf.com/stratany2013/public/schedule/detail/31380
  61. 61. Single : : : : : : S/W & H/W system security model management model metadata model audit model resource management model Common : storage & schema http://www.slideshare.net/cloudera/enterprise-data-hub-the-next-big-thing-in-big-data
  62. 62. Unifying Your Data Management Platform with Hadoop: Batch and Real-time Machine Data Ingest, Alerts, and Analytics http://strataconf.com/stratany2013/public/schedule/detail/30282
  63. 63. Last generation of data management is not sufficient More copies, representations, transformations increase risk Index once and reuse across workloads, lifecycle NoSQL: indexing and updates for interactive apps Hadoop: staging, persistence, and analytics Data Governance for Regulated Industries Using Hadoop http://strataconf.com/stratany2013/public/schedule/detail/30738
  64. 64. Data Intelligence Rethink How You See Data Sharmila Shahani-Mulligan (ClearStory Data) http://www.youtube.com/watch?v=07hGulTOZGk (9 minutes 6 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31742
  65. 65. The Data Availability Problem ? Access Question Sampling Analysis & Disc Modeling overy Loading Insight Data Prep – too slow! Information Supply Chain Introducing a New Way to Interact with Insight http://strataconf.com/stratany2013/public/schedule/detail/31743 Presentation
  66. 66. Running Non-MapReduce Big Data applications on Apache Hadoop http://strataconf.com/stratany2013/public/schedule/detail/30755
  67. 67. Apache HBase for Architects http://strataconf.com/stratany2013/public/schedule/detail/30619 What’s Next for Apache HBase: Multi-tenancy, Predictability, and Extensions. http://strataconf.com/stratany2013/public/schedule/detail/30857
  68. 68. Securing the Apache Hadoop Ecosystem http://strataconf.com/stratany2013/public/schedule/detail/30302
  69. 69. An Introduction to the Berkeley Data Analytics Stack With Spark, Spark Streaming, Shark, Tachyon, and BlinkDB http://strataconf.com/stratany2013/public/schedule/detail/30959
  70. 70. Schema Information does not exist until a schema is defined and data is stored in a relational database - anonymous Building a Data Platform http://strataconf.com/stratany2013/public/schedule/detail/31400
  71. 71. Lessons Learned From A Decade’s Worth of Big Data At The U.S. National Security Agency (NSA) http://strataconf.com/stratany2013/public/schedule/detail/30913
  72. 72. Managing a Rapidly Evolving Analytics Pipeline http://strataconf.com/stratany2013/public/schedule/detail/30635
  73. 73. Managing a Rapidly Evolving Analytics Pipeline http://strataconf.com/stratany2013/public/schedule/detail/30635
  74. 74. Stringer/Tez Shark SQL on/in Hadoop/Hbase Solutions Perception is Key: Telescopes, Microscopes and Data http://strataconf.com/strataeu2013/public/schedule/detail/32351
  75. 75. All SQL on Hadoop Solutions are Missing the Point of Hadoop Every Solution makes you define a schema - SQL(Structured Query Language) is expressed over an assumed schema Major reasons why Hadoop has taken of include: - Ability to load data without defining a schema - Process data using schema-on-read instead of first defining a schema Hadoop contains a lot of: - Raw, granular data sets with potentially inconsistent schemas - Data sets in JSON, key-value, and other self-describing (non-relational) models designed for schema-on-read processing SQL on Hadoop solutions that make you first define a schema are missing a major part of Hadoop’s usage patterns Flexible Schema and the End of ETL http://strataconf.com/stratany2013/public/schedule/detail/31868
  76. 76. Lessons Learned
  77. 77. Hadoop Adventures At Spotify http://strataconf.com/stratany2013/public/schedule/detail/30570
  78. 78. Hadoop Adventures At Spotify http://strataconf.com/stratany2013/public/schedule/detail/30570
  79. 79. Quick prototyping is the fastest way to internal advocacy. Ship It! Cloud == Speed We don’t always need a complicated solution. KISS Play to your differentiating strengths. Experience >> Data Bias towards impact. It Takes a Village EASE!! (Emulate, Analyze, Scale, Evaluate) How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce http://strataconf.com/stratany2013/public/schedule/detail/30707 Prototyping is key to overcoming resistance to change Technical architecture is heavily influenced by people organization Developing a team of experienced Hadoop users can often be done using internal employees A culture of experimentation and innovation yields the best result Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop http://strataconf.com/stratany2013/public/schedule/detail/30499
  80. 80. Questions? SELECT questions FROM audience;
  81. 81. References Strata Conference + Hadoop World 2013 Keynotes & Interviews http://www.youtube.com/playlist?list=PL055Epbe6d5ZtziVAooUC04i1hL_Z9Xvk Slides & Video http://strataconf.com/stratany2013/public/schedule/proceedings Tweets https://twitter.com/search?q=%23strataconf #strataconf
  82. 82. How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce http://strataconf.com/stratany2013/public/schedule/detail/30707 http://nordstrom.github.io/stratanyc/
  83. 83. http://complexdiagrams.com/properties Four Pillars of Visualization http://strataconf.com/stratany2013/public/schedule/detail/31182
  84. 84. Building a production machine learning infrastructure http://www.slideshare.net/joshwills/production-machine-learninginfrastructure
  85. 85. Text Analytics at Scale: Listening to 45 Million Customers http://strataconf.com/stratany2013/public/schedule/detail/30757
  86. 86. Words + Numbers = Insights Text Analytics at Scale: Listening to 45 Million Customers http://strataconf.com/stratany2013/public/schedule/detail/30757

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