Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Big Data Analytics
Search
Matt Wood
August 01, 2012
Technology
7
1.2k
Big Data Analytics
An introduction to Big Data Analytics in the cloud.
Matt Wood
August 01, 2012
Tweet
Share
More Decks by Matt Wood
See All by Matt Wood
Field Notes from Expeditions in the Cloud
mza
2
350
A Platform for Big Data
mza
6
710
The Data Lifecycle
mza
5
470
Provision Throughput Like a Boss
mza
0
410
Impact of Cloud Computing: Life Sciences
mza
2
820
Latency's Worst Nightmare: Performance Tuning Tips and Tricks
mza
4
1k
Under the Covers of DynamoDB
mza
4
960
From Analytics to Intelligence: Amazon Redshift
mza
9
960
Scaling Science
mza
3
460
Other Decks in Technology
See All in Technology
Plants vs thieves: Automated Tests in the World of Web Security
leichteckig
0
130
今から、 今だからこそ始める Terraform で Azure 管理 / Managing Azure with Terraform: The Perfect Time to Start
nnstt1
0
250
reinvent2024を起点に振り返るサーバーレスアップデート
mihonda
1
120
「隙間家具OSS」に至る道/Fujiwara Tech Conference 2025
fujiwara3
7
7k
20250122_FinJAWS
takuyay0ne
2
240
comilioとCloudflare、そして未来へと向けて
oliver_diary
6
490
Mocking your codebase without cursing it
gaqzi
0
110
インフラコストとセキュリティ課題解決のためのリアーキテクチャリング / srekaigi2025
hgsgtk
3
1.2k
一人から始めたSREチーム3年の歩み - 求められるスキルの変化とチームのあり方 - / The three-year journey of the SRE team, which started all by myself
vtryo
4
1.3k
Re:Define 可用性を支える モニタリング、パフォーマンス最適化、そしてセキュリティ
pyama86
8
1.7k
今年一年で頑張ること / What I will do my best this year
pauli
1
240
embedパッケージを深掘りする / Deep Dive into embed Package in Go
task4233
1
230
Featured
See All Featured
Building a Scalable Design System with Sketch
lauravandoore
460
33k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
120k
Side Projects
sachag
452
42k
Designing Experiences People Love
moore
139
23k
Why Our Code Smells
bkeepers
PRO
335
57k
The Illustrated Children's Guide to Kubernetes
chrisshort
48
49k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
28
4.5k
Being A Developer After 40
akosma
89
590k
Stop Working from a Prison Cell
hatefulcrawdad
267
20k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
59k
Building an army of robots
kneath
302
45k
GraphQLの誤解/rethinking-graphql
sonatard
68
10k
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