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Big Data Analytics
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Matt Wood
August 01, 2012
Technology
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1.3k
Big Data Analytics
An introduction to Big Data Analytics in the cloud.
Matt Wood
August 01, 2012
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Transcript
Big Data Analytics w i t h A m a
z o n W e b S e r v i c e s Dr. Matt Wood An Online Seminar for Partners. Wednesday 1st August.
Hello, and thank you.
Big Data Analytics An introduction
Big Data Analytics An introduction The story of analytics on
AWS
Big Data Analytics An introduction The story of analytics on
AWS Integrating partners
Big Data Analytics An introduction The story of analytics on
AWS Integrating partners Partner success stories
INTRODUCING BIG DATA 1
Data for competitive advantage.
Customer segmentation, financial modeling, system analysis, line-of-sight, business intelligence. Using
data
Generation Collection & storage Analytics & computation Collaboration & sharing
Cost of data generation is falling.
Generation Collection & storage Analytics & computation Collaboration & sharing
lower cost, increased throughput
Generation Collection & storage Analytics & computation Collaboration & sharing
HIGHLY CONSTRAINED
Very high barrier to turning data into information.
Move from a data generation challenge to analytics challenge.
Enter the Cloud.
Remove the constraints.
Enable data-driven innovation.
Move to a distributed data approach.
Maturation of two things.
Maturation of two things. Software for distributed storage and analysis
Maturation of two things. Software for distributed storage and analysis
Infrastructure for distributed storage and analysis
Frameworks for data-intensive workloads. Software Distributed by design.
Platform for data-intensive workloads. Infrastructure Distributed by design.
Support the data timeline.
Generation Collection & storage Analytics & computation Collaboration & sharing
HIGHLY CONSTRAINED
Generation Collection & storage Analytics & computation Collaboration & sharing
Lower the barrier to entry.
Accelerate time to market and increase agility.
Enable new business opportunities.
Washington Post Pinterest NASA
“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
THE STORY OF ANALYTICS 2
EC2 Utility computing. 6 years young.
Embarrassingly parallel problems. Scale out systems Queue based distribution. Small,
medium and high scale.
None
None
None
EC2 Utility computing. 6 years young. Cost optimization.
Achieving economies of scale 100% Time
Reserved capacity Achieving economies of scale 100% Time
Reserved capacity Achieving economies of scale 100% Time On-demand
Reserved capacity Achieving economies of scale 100% Time On-demand UNUSED
CAPACITY
Bid on unused EC2 capacity. Spot Instances Very large discount.
Perfect for batch runs. Balance cost and scale.
$650 per hour
Pattern for distributed computing. Map/reduce Software frameworks such as Hadoop.
Write two functions. Scale up.
Pattern for distributed computing. Map/reduce Software frameworks such as Hadoop.
Write two functions. Scale up. Complex cluster configuration and management.
Managed Hadoop clusters. Amazon Elastic MapReduce Easy to provision and
monitor. Write two functions. Scale up. Optimized for S3 access.
Input data S3 UNDER THE HOOD i i
Elastic MapReduce Code Input data S3 UNDER THE HOOD i
i
Elastic MapReduce Code Name node Input data S3 UNDER THE
HOOD i i
Elastic MapReduce Code Name node Input data S3 Elastic cluster
UNDER THE HOOD i i
Elastic MapReduce Code Name node Input data S3 Elastic cluster
HDFS UNDER THE HOOD i i
Elastic MapReduce Code Name node Input data S3 Elastic cluster
HDFS Queries + BI Via JDBC, Pig, Hive UNDER THE HOOD i i
Elastic MapReduce Code Name node Output S3 + SimpleDB Input
data S3 Elastic cluster HDFS Queries + BI Via JDBC, Pig, Hive UNDER THE HOOD i i
Output S3 + SimpleDB Input data S3 UNDER THE HOOD
i i
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Performance
Performance Compute performance
Intel Xeon E5-2670 Cluster Compute 10 gig E non-blocking network
Placement groupings 60.5 Gb UNDER THE HOOD i i
Intel Xeon E5-2670 Cluster Compute 10 gig E non-blocking network
Placement groupings 60.5 Gb UNDER THE HOOD i i + GPU enabled instances
Performance Compute performance
Performance Compute performance IO performance
NoSQL Unstructured data storage.
Predictable, consistent performance DynamoDB Unlimited storage No schema for unstructured
data Single digit millisecond latencies Backed on solid state drives
...and SSDs for all. New Hi1 storage instances.
2 x 1Tb SSDs hi1.4xlarge 10 GigE network HVM: 90k
IOPS read, 9k to 75k write PV: 120k IOPS read, 10k to 85k write UNDER THE HOOD i i
Netflix “The hi1.4xlarge configuration is about half the system cost
for the same throughput.” http://techblog.netflix.com/2012/07/benchmarking-high-performance-io-with.html
EBS Elastic Block Store
Provisioned IOPS Provision required IO performance
Provisioned IOPS Provision required IO performance + EBS-optimized instances with
dedicated throughput
Generation Collection & storage Analytics & computation Collaboration & sharing
Performance + ease of use
PARTNER INTEGRATION 3
Extend platform with partners
Innovate on behalf of customers
Remove undifferentiated heavy lifting
Rolled the Amazon Hadoop optimizations into MapR MapR distribution for
EMR Choice for EMR customers Easy deployment for MapR customers
Hadoop distribution MapR distribution for EMR Integrated into EMR NFS
and ODBC drivers High availability and cluster mirroring
Enterprise data toolchain Informatica on EMR “Swiss army knife” for
data formats Data integration Available to all on EMR
AWS Marketplace Karmasphere, Marketshare, Acunu Cassandra, Metamarkets, Aspera and more.
aws.amazon.com/marketplace
PARTNER SUCCESS STORIES 4
Razorfish
3.5 billion records 71MM unique cookies 1.7MM targeted ads per
day
3.5 billion records 71MM unique cookies 1.7MM targeted ads per
day 500% improvement in return on ad spend.
Cycle Computing + Schrodinger
30k cores, $4200 an hour (compared to $10+ million)
Marketshare + Ticketmaster Optimize live event pricing
Reduced developer infrastructure management time by 3 hours a day
Thank you!
Q & A
[email protected]
@mza on Twitter