Big Data Analytics with Amazon Web Services
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Slides from the online seminar, August 1st 2012.
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Big Data Analytics with Amazon Web Services
- 1. Big Data Analytics with Amazon Web Services Dr. Matt Wood An Online Seminar for Partners. Wednesday 1st August.
- 2. Hello, and thank you.
- 3. Big Data Analytics An introduction
- 4. Big Data Analytics An introduction The story of analytics on AWS
- 5. Big Data Analytics An introduction The story of analytics on AWS Integrating partners
- 6. Big Data Analytics An introduction The story of analytics on AWS Integrating partners Partner success stories
- 8. Data for competitive advantage.
- 9. Using data Customer segmentation, financial modeling, system analysis, line-of-sight, business intelligence.
- 10. Generation Collection & storage Analytics & computation Collaboration & sharing
- 11. Cost of data generation is falling.
- 12. lower cost, increased throughput Generation Collection & storage Analytics & computation Collaboration & sharing
- 13. Generation HIGHLY CONSTRAINED Collection & storage Analytics & computation Collaboration & sharing
- 14. Very high barrier to turning data into information.
- 15. Move from a data generation challenge to analytics challenge.
- 16. Enter the Cloud.
- 19. Move to a distributed data approach.
- 20. Maturation of two things.
- 21. Software for distributed storage and analysis Maturation of two things.
- 22. Software for distributed storage and analysis Maturation of two things. Infrastructure for distributed storage and analysis
- 23. Software Frameworks for data-intensive workloads. Distributed by design.
- 24. Infrastructure Platform for data-intensive workloads. Distributed by design.
- 26. Generation HIGHLY CONSTRAINED Collection & storage Analytics & computation Collaboration & sharing
- 27. Generation Collection & storage Analytics & computation Collaboration & sharing
- 28. Lower the barrier to entry.
- 29. Accelerate time to market and increase agility.
- 30. Enable new business opportunities.
- 31. Washington Post Pinterest NASA
- 32. “AWS enables Pfizer to explore difficult or deep scientific questions in a timely, scalable manner and helps us make better decisions more quickly” Michael Miller, Pfizer
- 33. 2 THE STORY OF ANALYTICS
- 34. EC2 Utility computing. 6 years young.
- 35. Scale out systems Embarrassingly parallel problems. Queue based distribution. Small, medium and high scale.
- 39. Cost optimization. EC2 Utility computing. 6 years young.
- 40. Achieving economies of scale 100% Time
- 41. Achieving economies of scale 100% Reserved capacity Time
- 42. Achieving economies of scale 100% On-demand Reserved capacity Time
- 43. Achieving economies of scale UNUSED CAPACITY 100% On-demand Reserved capacity Time
- 44. Spot Instances Bid on unused EC2 capacity. Very large discount. Perfect for batch runs. Balance cost and scale.
- 45. $650 per hour
- 46. Map/reduce Pattern for distributed computing. Software frameworks such as Hadoop. Write two functions. Scale up.
- 47. Map/reduce Pattern for distributed computing. Software frameworks such as Hadoop. Write two functions. Scale up. Complex cluster configuration and management.
- 48. Amazon Elastic MapReduce Managed Hadoop clusters. Easy to provision and monitor. Write two functions. Scale up. Optimized for S3 access.
- 49. S3 Input data UNDER THE i i HOOD
- 50. S3 Input data Code Elastic MapReduce UNDER THE i i HOOD
- 51. S3 Input data Code Elastic Name MapReduce node UNDER THE i i HOOD
- 52. S3 Input data Code Elastic Name MapReduce node Elastic cluster UNDER THE i i HOOD
- 53. S3 Input data Code Elastic Name MapReduce node HDFS Elastic cluster UNDER THE i i HOOD
- 54. S3 Input data Code Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster UNDER THE i i HOOD
- 55. S3 Input data Code Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster UNDER THE i i HOOD
- 56. S3 Input data Output S3 + SimpleDB UNDER THE i i HOOD
- 71. Performance
- 73. UNDER THE i i Cluster Compute HOOD Intel Xeon E5-2670 10 gig E non-blocking network 60.5 Gb Placement groupings
- 74. UNDER THE i i Cluster Compute HOOD Intel Xeon E5-2670 10 gig E non-blocking network 60.5 Gb Placement groupings + GPU enabled instances
- 76. IO performance Performance Compute performance
- 78. DynamoDB Predictable, consistent performance Unlimited storage Single digit millisecond latencies No schema for unstructured data Backed on solid state drives
- 79. ...and SSDs for all. New Hi1 storage instances.
- 80. UNDER THE i i hi1.4xlarge HOOD 2 x 1Tb SSDs 10 GigE network HVM: 90k IOPS read, 9k to 75k write PV: 120k IOPS read, 10k to 85k write
- 81. “The hi1.4xlarge configuration is about half the system cost for the same throughput.” Netflix http://techblog.netflix.com/2012/07/benchmarking-high-performance-io-with.html
- 83. Provisioned IOPS Provision required IO performance
- 84. Provisioned IOPS Provision required IO performance + EBS-optimized instances with dedicated throughput
- 85. Generation Collection & storage Analytics & computation Collaboration & sharing
- 86. Performance + ease of use
- 88. Extend platform with partners
- 89. Innovate on behalf of customers
- 90. Remove undifferentiated heavy lifting
- 91. MapR distribution for EMR Rolled the Amazon Hadoop optimizations into MapR Choice for EMR customers Easy deployment for MapR customers
- 92. MapR distribution for EMR Hadoop distribution Integrated into EMR NFS and ODBC drivers High availability and cluster mirroring
- 93. Informatica on EMR Enterprise data toolchain “Swiss army knife” for data formats Data integration Available to all on EMR
- 94. AWS Marketplace Karmasphere, Marketshare, Acunu Cassandra, Metamarkets, Aspera and more. aws.amazon.com/marketplace
- 96. Razorfish
- 97. 3.5 billion records 71MM unique cookies 1.7MM targeted ads per day
- 98. 3.5 billion records 71MM unique cookies 1.7MM targeted ads per day 500% improvement in return on ad spend.
- 99. Cycle Computing + Schrodinger
- 100. 30k cores, $4200 an hour (compared to $10+ million)
- 101. Marketshare + Ticketmaster Optimize live event pricing
- 102. Reduced developer infrastructure management time by 3 hours a day
- 103. Thank you!
- 104. Q&A matthew@amazon.com @mza on Twitter