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BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
BDT201 AWS Data Pipeline - AWS re: Invent 2012
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BDT201 AWS Data Pipeline - AWS re: Invent 2012

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In this session, we'll review the features and architecture of the new AWS Data Pipeline service and explain how you can use it to better manage your data-driven workloads. We'll then go over a few …

In this session, we'll review the features and architecture of the new AWS Data Pipeline service and explain how you can use it to better manage your data-driven workloads. We'll then go over a few examples of setting up and provisioning a pipeline in the system.

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  • 1. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 2. Amazon DynamoDB Amazon S3
  • 3. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 4. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 5. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 6. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 7. Amazon S3 Amazon RDS Amazon AmazonDynamoDB Redshift HDFS On (Amazon EMR) Premise
  • 8. Input DatanodeActivity[Output Datanode]
  • 9. Input Datanode with precondition checkActivity with failure & delay notificationsOuput Datanode
  • 10. Data DataData Stores Data Stores Compute Resources
  • 11. Start Interval[End]
  • 12. Noon Today 1 hour
  • 13. 12-1pm X1-2pm2-3pm …..
  • 14. 12-1pm X1-2pm2-3pm X 1 day …..
  • 15. Monthly DailyHourly Quarterly Yearly Weekly
  • 16. S3 logs (hourly) Geolocation data Per-geographyusage computation (hourly) Redshift results
  • 17. S3 logs (hourly) Geolocation dataPrecondition: files exist Precondition: ./geo_available Per-geography usage computation (hourly) Redshift results
  • 18. Dynamo RDSevent data demographics Hive-basedanalysis (hourly) Redshift results
  • 19. Hourly click updates Hourly event analysis Daily reporting SQL
  • 20. Custom Amazon RDS Amazon S3 Amazon demographics logs Precondition DynamoDB event data Hive scriptEMR usage-by-geo job Amazon Redshift DW table Amazon Redshift Amazon EC2 DW table report generation
  • 21. Custom Amazon RDS Amazon S3 Amazon demographics logs Precondition DynamoDB event data Hive scriptEMR usage-by-geo job Amazon Redshift DW table Amazon Redshift Amazon EC2 DW table report generation
  • 22. We Manage You Manage EMR Clusters EC2 EC2 Instances InstancesEMR Clusters On Premise Resources
  • 23. { "objects" : [ { "name" : “My Copy”, "type" : “Copy Action”, “input”: {“ref” : “My RDS Data”}, “output”: {“ref” : “My S3 Data”}, ”runsOn” : {“ref”: “My Instance”}, "schedule" : { "ref" : “My Schedule" } }, { "name" : ”My Instance”, "type" : ”EC2Instance”, "instanceType" : "m1.small”, "schedule" : { "ref” : “My Schedule" } },…..}
  • 24. On AWS On PremiseHigh $1/month $2.50/monthFrequencyLow Frequency $.60/month $1.50/month
  • 25. We are sincerely eager to hear your feedback on thispresentation and on re:Invent. Please fill out an evaluation form when you have a chance.

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