Big Data in the Microsoft Platform
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Big Data in the Microsoft Platform






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Big Data in the Microsoft Platform Presentation Transcript

  • 1. Building Big Data Solutions inthe Microsoft PlatformJesus RodriguezTellago, Inc, Tellago Studios
  • 2. Big Data?
  • 3. About Me…• Hackerpreneur• Co-Founder Tellago, Tellago Studios, Inc.• Microsoft Architect Advisor• Microsoft MVP• Oracle ACE• Speaker, Author•••
  • 4. Agenda• Big Data Overview• MS HDInsight – Map Reduce – HDFS – Hive – Pig – Sqoop• HDInsight Service• The Hadoop Ecosystem• The Future….
  • 5. Big Data?• A bunch of data?• An industry?• An expertise?• A trend?• A cliché?
  • 6. A Clue?• 2008: Google processes 20 PB a day• 2009: Facebook has 2.5 PB user data + 15 TB/day• 2009: eBay has 6.5 PB user data + 50 TB/day• 2011: Yahoo! has 180-200 PB of data• 2012: Facebook ingests 500 TB/day
  • 7. We Love Data!
  • 8. But...
  • 9. Processing Large Amounts of Data is Complicated....
  • 10. Sucessful Big Data = Scalable Computing + Large Storage
  • 11. A Trivial Model
  • 12. Not So Fast....
  • 13. Parallel Data Computing is Complicated
  • 14. So Is Large Data Storage
  • 15. Enter the World of Hadoop...
  • 16. Hadoop Design Principles• System Shall Manage and Heal Itself• Performance Shall Scale Linearly• Compute Shall Move to Data• Simple Core, Modular and Extensible
  • 17. Hadoop History• 2002-2004: Doug Cutting and Mike Cafarella started working on Nutch• 2003-2004: Google publishes GFS and MapReduce papers• 2004: Cutting adds DFS & MapReduce support to Nutch• 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch• 2007: NY Times converts 4TB of archives over 100 EC2s• 2008: Web-scale deployments at Y!, Facebook,• April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910 nodes• May 2009: – Yahoo does fastest sort of a TB, 62secs over 1460 nodes – Yahoo sorts a PB in 16.25hours over 3658 nodes• June 2009, Oct 2009: Hadoop Summit, Hadoop World• September 2009: Doug Cutting joins Cloudera
  • 18. Hadoop Ecosystem ETL Tools BI Reporting RDBMSZookeepr (Coordination) Pig (Data Flow) Hive (SQL) Sqoop Avro (Serialization) MapReduce (Job Scheduling/Execution System) HBase (key-value store) (Streaming/Pipes APIs) HDFS (Hadoop Distributed File System)
  • 19. Microsoft & Hadoop
  • 20. HDInsight
  • 21. HDFS
  • 22. HDFS Is…• A distributed file system• Redundant storage• Designed to reliably store data using commodity hardware• Designed to expect hardware failures• Intended for large files• Designed for batch inserts• The Hadoop Distributed File System
  • 23. HDFS at a Glance Block Size = 64MB Replication Factor = 3Cost/GB is a few ¢/month vs $/month
  • 24. HDInsightHDFSDemo
  • 25. Map Reduce
  • 26. Map Reduce Is…• A programming model for expressing distributed computations at a massive scale• An execution framework for organizing and performing such computations• An open-source implementation called Hadoop
  • 27. Map Reduce At a Glance
  • 28. HDInsightMap Reduce Demo
  • 29. Hive
  • 30. Hive Is…• A system for managing and querying structured data built on top of Hadoop – Map-Reduce for execution – HDFS for storage – Metadata on raw files• Key Building Principles: – SQL as a familiar data warehousing tool – Extensibility – Types, Functions, Formats, Scripts – Scalability and Performance
  • 31. Hive Architecture
  • 32. HDInsightHacking with Hive
  • 33. Pig
  • 34. Pig Is…Apache Pig is a platform for analyzing large data sets that consists of a high-level language (PigLatin) for expressing data analysis programs, coupled with infrastructure for evaluating these programs.• Ease of programming• Optimization opportunities• Extensibility• Built upon Hadoop
  • 35. Pig Architecture Grunt (Interactive shell) PigServer (Java API) Parser (PigLatinLogicalPlan) Optimizer (LogicalPlan  LogicalPlan)Pig Context Compiler (LogicalPlan  PhysiclaPlan  MapReducePlan) ExecutionEngine Hadoop
  • 36. HDInsightRocking Data Processing with Pig
  • 37. Sqoop
  • 38. Sqoop Is…• Easy import of data from many databases to HDFS• Generates code for use in MapReduce applications• Integrates with Hive
  • 39. Sqoop Architecture
  • 40. HDInsightBulk Data Loading UsingSqoop
  • 41. HDInsight Service
  • 42. HDInsight Service Architecture
  • 43. HDInsightHDInsight Service Overview
  • 44. Hadoop Considerations
  • 45. Super Crowded Ecosystem
  • 46. The Hadoop Ecosystem
  • 47. Hadoop is not a silver bullet...
  • 48. Some Challenges• Hadoop doesn’t power big data applications – Not a transactional datastore. Slosh back and forth via ETL• Processing latency – Non-incremental, must re-slurp entire dataset every pass• Ad-Hoc queries – Bare metal interface, data import• Graphs – Only a handful of graph problems amenable to MR
  • 49. Beyond Hadoop• Percolator(incremental processing)• Dremel(ad-hoc analysis queries)• Pregel (Big graphs)
  • 50. In the Meantime...
  • 51. Takeaways• Hadoop provides the foundation of big data solutions• Computing and storage are the fundamental components of Hadoop• HDInsight Server and Service are Microsoft’s distributions of Hadoop• HDInsight is just one component of Microsoft’s BI strategy
  • 52. Thanks http://jrodthoughts.com!/jrodthoughts