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Rhat OSS - Cloudera - Mike Olson - Hadoop Data Analytics In The Cloud

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Mike Olson's talk on Hadoop Data Analytics at the O'Reilly Open Source Convention

Mike Olson's talk on Hadoop Data Analytics at the O'Reilly Open Source Convention

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  • 1. Hadoop Data Analytics in the Cloud Mike Olson Chief Executive Officer Friday, July 17, 2009
  • 2. Hadoop History ▪ Doug Cutting worked on Nutch (web-scale crawler-based search), 2002-2004 ▪ Google published MapReduce paper in 2004 ▪ Cutting adds DFS & MapReduce support to Nutch ▪ Joined by Mike Cafarella ▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch ▪ Web-scale deployments in 2007, 2008 at Y!, Facebook, others ▪ Today: 22 committers to core project ▪ Related projects: HBase, Hive, Pig, Mahout, Hama and others Friday, July 17, 2009
  • 3. Why Hadoop? ▪ Large web properties invented MapReduce for large-scale, reliable, inexpensive analytics ▪ Enterprises generally need these techniques ▪ Retail, financial services, oil and gas, health care, green technologies and more ▪ Hardware trends driving toward long-term retention of valuable source data ▪ New analytical tools are required ▪ Hadoop complements current-generation data warehousing and analytical products Friday, July 17, 2009
  • 4. Where Does Data Come From? Many Sources Provide Deeper Insight Friday, July 17, 2009
  • 5. Where Does Data Come From? Many Sources Provide Deeper Insight ▪ Simulations and Scientific/Experimental Data ▪ genome sequencing, medical imaging, wireless sensors Friday, July 17, 2009
  • 6. Where Does Data Come From? Many Sources Provide Deeper Insight ▪ Simulations and Scientific/Experimental Data ▪ genome sequencing, medical imaging, wireless sensors ▪ Existing Databases ▪ product catalogs, historical sales data, transaction histories Friday, July 17, 2009
  • 7. Where Does Data Come From? Many Sources Provide Deeper Insight ▪ Simulations and Scientific/Experimental Data ▪ genome sequencing, medical imaging, wireless sensors ▪ Existing Databases ▪ product catalogs, historical sales data, transaction histories ▪ User Data ▪ web logs, clicks on website, pictures, videos, bbs, etc Friday, July 17, 2009
  • 8. Where Does Data Come From? Many Sources Provide Deeper Insight ▪ Simulations and Scientific/Experimental Data ▪ genome sequencing, medical imaging, wireless sensors ▪ Existing Databases ▪ product catalogs, historical sales data, transaction histories ▪ User Data ▪ web logs, clicks on website, pictures, videos, bbs, etc ▪ System Generated Data ▪ 1000’s of systems reporting status every second Friday, July 17, 2009
  • 9. Where Does Data Come From? Many Sources Provide Deeper Insight ▪ Simulations and Scientific/Experimental Data ▪ genome sequencing, medical imaging, wireless sensors ▪ Existing Databases ▪ product catalogs, historical sales data, transaction histories ▪ User Data ▪ web logs, clicks on website, pictures, videos, bbs, etc ▪ System Generated Data ▪ 1000’s of systems reporting status every second ▪ Data Comes in All Shapes, Sizes, Schemas and Structures ▪ Hadoop combines many sources regardless of format and structure Friday, July 17, 2009
  • 10. Hadoop Technical Overview: HDFS Storing Data: Distributed Over Many Machines HDFS: Hadoop Distributed File System Friday, July 17, 2009
  • 11. Hadoop Technical Overview: HDFS Storing Data: Distributed Over Many Machines HDFS: Hadoop Distributed File System Friday, July 17, 2009
  • 12. Hadoop Technical Overview: HDFS Storing Data: Distributed Over Many Machines Commodity Servers HDFS: Hadoop Distributed File System Friday, July 17, 2009
  • 13. Hadoop Technical Overview: HDFS Storing Data: Distributed Over Many Machines Commodity Servers Files are broken into blocks and distributed across all servers. Replication protects data from hardware failure. HDFS: Hadoop Distributed File System Friday, July 17, 2009
  • 14. Hadoop Technical Overview: MapReduce Processing Data: Leveraging Data Locality MapReduce Friday, July 17, 2009
  • 15. Hadoop Technical Overview: MapReduce Processing Data: Leveraging Data Locality MapReduce Friday, July 17, 2009
  • 16. Hadoop Technical Overview: MapReduce Processing Data: Leveraging Data Locality MapReduce Friday, July 17, 2009
  • 17. Hadoop Technical Overview: MapReduce Processing Data: Leveraging Data Locality Data elements processed locally, in parallel Reliable computation implicitly managed by Hadoop MapReduce Friday, July 17, 2009
  • 18. Hadoop Technical Overview: Reliability Fault Tolerance: Handled with Software Software Fault Tolerance Friday, July 17, 2009
  • 19. Hadoop Technical Overview: Reliability Fault Tolerance: Handled with Software Software Fault Tolerance Friday, July 17, 2009
  • 20. Hadoop Technical Overview: Reliability Fault Tolerance: Handled with Software Data loss prevented through automatic replication and rebalancing Computation is restarted automatically without user intervention Software Fault Tolerance Friday, July 17, 2009
  • 21. Cloud Deployment Options for Hadoop ▪ In your data center • Acquire, provision, administer servers • Choose a virtualization infrastructure? ▪ On dedicated, hosted services • Scale up or down by coordinating with your MSP • On dynamic web services (AWS and others) • Spin up, use, shut down a cluster • Issues: • Data persistence and location, organizational control Friday, July 17, 2009
  • 22. (c) 1009 Cloudera, Inc. or its licensors.  "Cloudera" is a registered trademark of Cloudera, Inc.. All rights reserved. Friday, July 17, 2009