Big data accessible to all Ryan Shuttleworth, AWS Evangelist
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam quis ligula neque, eget venenatis sem. Suspendisse non eros nulla, at placerat nibh. Very large dataset seeks strong & consistent compute for short term relationship, possibly longer. GSOH a plus aws.amazon.com Personal Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam quis ligula neque, eget venenatis sem. Suspendisse non eros nulla, at placerat nibh. Cras id lectus mattis est ullamcorper blandit. Proin ut nisi vitae enim vulputate tempor. Phasellus id commodo eros. Mauris nec dignissim turpis. Nunc Cras id lectus mattis est ullamcorper blandit. Proin ut nisi vitae enim vulputate tempor. Phasellus id commodo eros. Mauris nec dignissim turpis. Nunc Bring compute capacity to the data Compute Storage Big Data Cras id lectus mattis est ullamcorper blandit. Proin ut nisi vitae enim vulputate tempor. Phasellus id commodo eros. Mauris nec dignissim turpis. Nunc
Data App Data has gravity Compute Storage Big Data
Data App latency Throughput …and inertia at volume… Compute Storage Big Data
Data App …easier to move applications to the data Compute Storage Big Data
From one instance… Compute Storage Big Data
…to thousands Compute Storage Big Data
and back again… Compute Storage Big Data
have data
can store have data
can storecan analyse have data
economically
fast
Who is your customer really? What do people really like? What is happening socially with your products? How do people really use your products? Consumer
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Lesson 1: don’t leave your Amazon account logged in at home
Lesson 1: don’t leave your Amazon account logged in at home Lesson 2: Drive proactive business processes based upon data you have
1 instance for 100 hours = 100 instances for 1 hour
Small instance = $8
1 instance for 1,000 hours = 1,000 instances for 1 hour
Small instance = $80
100 % Achieving economies of scale Time
Reserved capacity 100 % Achieving economies of scale Time
On Reserved capacity 100% On-demand Time Achieving economies of scale
On Reserved capacity 100% On-demand Time Achieving economies of scale Spot
Allocate 4 instances Job Flow 14 Hours Duration: Scenario #1 EMR with spot instances #1: Cost without Spot 4 instances *14 hrs * $0.50 = $28
Allocate 4 instances Job Flow 14 Hours Duration: Scenario #1 Add 5 Spot Instances Duration: Job Flow 7 Hours Scenario #2 EMR with spot instances #1: Cost without Spot 4 instances *14 hrs * $0.50 = $28
Allocate 4 instances Job Flow 14 Hours Duration: Scenario #1 Add 5 Spot Instances Duration: Job Flow 7 Hours Scenario #2 EMR with spot instances #1: Cost without Spot 4 instances *14 hrs * $0.50 = $28 #2: Cost with Spot 4 instances *7 hrs * $0.50 = $ instances * 7 hrs * $0.25 = $8.75 Total = $22.75
Allocate 4 instances Job Flow 14 Hours Duration: Scenario #1 Add 5 Spot Instances Duration: Job Flow 7 Hours Scenario #2 EMR with spot instances #1: Cost without Spot 4 instances *14 hrs * $0.50 = $28 #2: Cost with Spot 4 instances *7 hrs * $0.50 = $ instances * 7 hrs * $0.25 = $8.75 Total = $22.75 Time Savings: 50% Cost Savings: ~22%
Introducing Amazon Redshift Data Warehousing the AWS Way Easily and rapidly analyze petabytes of data 1/10 the cost of traditional data warehouses Automated deployment & administration Compatible with popular BI tools
Internal Testing: At Least 10X Faster for a Fraction of the Cost 2 billion row data set & 6 most complex queries 32 nodes, 4.2 TB of RAM, 1.6 PB of disk Several million dollars Two 16 TB / 128 GB RAM nodes $3.65 / hour Amazon Redshift Our Test On-premises retail data warehouse
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Big Data Cloud Economics Utility Performant Pay-as-you-goAccessible Low priceUnlimited