BigData processing in the cloud – Guest Lecture - University of Applied Sciences Rapperswil - 29.4.14

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  • 1. © 2013 IBM Corporation1 BigData processing in the cloud – Guest Lecture - University of Applied Sciences Rapperswil - 29.4.14 Romeo Kienzler IBM Innovation Center Source: http://res.sys-con.com/story/oct12/2398990/Cloud_BigData_468.jpg
  • 2. © 2013 IBM Corporation2 What is BIG data?
  • 3. © 2013 IBM Corporation3 What is BIG data?
  • 4. © 2013 IBM Corporation4 What is BIG data? Big Data Hadoop
  • 5. © 2013 IBM Corporation5 What is BIG data? Business Intelligence Data Warehouse
  • 6. © 2013 IBM Corporation6 Map-Reduce → Hadoop → BigInsights
  • 7. © 2013 IBM Corporation7 BigData UseCases ● Google Index ● 40 X 10^9 = 40.000.000.000 => 40 billion pages indexed ● Will break 100 PB barrier soon ● Derived from MapReduce ● now “caffeine” based on “percolator” ● Incremental vs. batch ● In-Memory vs. disk
  • 8. © 2013 IBM Corporation8 BigData UseCases ● CERN LHC ● 25 petabytes per year ● Facebook ● Hive Datawarehouse ● 300 PB, growing 600 TB / d ● > 100 k servers ● Genomics ● Enterprises ● Data center analytics (Logflies, OS/NW monitors, ...) ● Predictive Maintenance, Cybersecurity ● Social Media Analytics ● DWH offload ● Call Detail Record (CDR) data preservation http://www.balthasar-glaettli.ch/vorratsdaten/
  • 9. © 2013 IBM Corporation9 BigData Analytics
  • 10. © 2013 IBM Corporation10 BigData Analytics – Predictive Analytics "sometimes it's not who has the best algorithm that wins; it's who has the most data." (C) Google Inc. The Unreasonable Effectiveness of Data¹ ¹http://www.csee.wvu.edu/~gidoretto/courses/2011-fall-cp/reading/TheUnreasonable%20EffectivenessofData_IEEE_IS2009.pdf No Sampling => Work with full dataset => No p-Value/z-Scores anymore
  • 11. © 2013 IBM Corporation11 Data Parallelism
  • 12. © 2013 IBM Corporation12 Aggregated Bandwith between CPU, Main Memory and Hard Drive 1 TB (at 10 GByte/s) - 1 Node - 100 sec - 10 Nodes - 10 sec - 100 Nodes - 1 sec - 1000 Nodes - 100 msec
  • 13. © 2013 IBM Corporation13 Fault Tolerance / Commodity Hardware AMD Turion II Neo N40L (2x 1,5GHz / 2MB / 15W), 8 GB RAM, 3TB SEAGATE Barracuda 7200.14 < CHF 500  100 K => 200 X (2, 4, 3) => 400 Cores, 1,6 TB RAM, 200 TB HD  MTBF ~ 365 d > 1,5 d Source: http://www.cloudcomputingpatterns.org/Watchdog
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  • 16. © 2013 IBM Corporation16 HDFS – Hadoop File System
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  • 35. © 2013 IBM Corporation35 Map-Reduce Source: http://www.cloudcomputingpatterns.org/Map_Reduce
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  • 77. © 2013 IBM Corporation77 What role is the cloud playing here?
  • 78. © 2013 IBM Corporation78 “Elastic” Scale-Out Source: http://www.cloudcomputingpatterns.org/Continuously_Changing_Workload
  • 79. © 2013 IBM Corporation79 “Elastic” Scale-Out of
  • 80. © 2013 IBM Corporation80 “Elastic” Scale-Out of CPU Cores
  • 81. © 2013 IBM Corporation81 “Elastic” Scale-Out of CPU Cores Storage
  • 82. © 2013 IBM Corporation82 “Elastic” Scale-Out of CPU Cores Storage
  • 83. © 2013 IBM Corporation83 “Elastic” Scale-Out of CPU Cores Storage Memory
  • 84. © 2013 IBM Corporation84 “Elastic” Scale-Out of CPU Cores Storage Memory
  • 85. © 2013 IBM Corporation85 “Elastic” Scale-Out linear Source: http://www.cloudcomputingpatterns.org/Elastic_Platform
  • 86. © 2013 IBM Corporation86 “Elastic” Scale-Out linear Source: http://www.cloudcomputingpatterns.org/Elastic_Platform
  • 87. © 2013 IBM Corporation87 BigData Scale-Out How do Databases Scale-Out?
  • 88. © 2013 IBM Corporation88 BigData Scale-Out How do Databases Scale-Out?
  • 89. © 2013 IBM Corporation89 How do Databases Scale-Out? Shared Disk Architectures
  • 90. © 2013 IBM Corporation90 How do Databases Scale-Out? Shared Disk Architectures
  • 91. © 2013 IBM Corporation91 How do Databases Scale-Out? Shared Nothing Architectures
  • 92. © 2013 IBM Corporation92 Born on the cloud Databases Source: http://www.constructioncloudcomputing.com/wp-content/uploads/2010/10/dreamstime_7360880-480x300.jpg Source: http://www.cloudcomputingpatterns.org/Execution_Environment
  • 93. © 2013 IBM Corporation93 Google AppEngine Google App Engine is a Platform as a Service (PaaS) offering that lets you build and run applications on Google’s infrastructure. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs change. With App Engine, there are no servers for you to maintain. You simply upload your application and it’s ready to go. Source: http://www.cloudcomputingpatterns.org/Platform_as_a_Service_%28PaaS%29
  • 94. © 2013 IBM Corporation94 Google AppEngine Database Services
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  • 96. © 2013 IBM Corporation96 IBM BlueMix BlueMix is a Platform as a Service Cloud, based on Cloud Foundry, employing Enterprise grade services enriched with IBM Software and hosted at SOFTLAYER
  • 97. © 2013 IBM Corporation97 IBM BlueMix, a Cloudfoundry runtime Linux VM Linux VM Code Runtime Framework+ Droplet Linux VM Container Container Container SQL Push SSO Services: ... DropletDroplet
  • 98. © 2013 IBM Corporation98 ● Summary ● BigData is born on the cloud ● Cloud facilitates resource provisioning, configuration and deployment ● Highly innovative area ● Technology ● UseCases ● Links ● http://en.wikipedia.org/wiki/MapReduce ● http://www.se-radio.net/2013/12/episode-199-michael-stonebraker/ ● Sign up for the free BlueMix beta ● http://bluemix.net ● Come to the BlueMix Days ● http://bit.ly/1lsIY8J ● Use our software ● Biginsights: http://www.ibm.com/software/data/infosphere/biginsights/quick-start/