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Jose Papo
Amazon Evangelist
@josepapo
@josepapo
HANDS-ON DEMOS
AFTER THE BIG
DATA SESSION
La Nube es el driver de las nuevas tendencias tecnológicas
Accelerating the startup boom
Optimizing the corporate world
#1
●○○○○
We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.
We are constantly producing more data
We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.
From all types of industries
Collect,
Store,
Organize,
Analyze &
Share
3Vs
27 TB per day
Large Hadron Collider – CERN
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
The Role of Data
is Changing
We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.
Until now, Questions you ask drove Data model
New model is collect as much data as possible
– “Data-First Philosophy”
We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.
Data is the new raw material for
any business on par with
capital, people, labor
Datais the new raw material for business on par with capital
& labor
Data
Actionable Information
Generated
data
Available for analysis
Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011
IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares
Data Strategist
1.1M peak
requests/sec
lunch hours last year?
select productId, count(*)
from page_hits
where hour in (12,13)
group by productId
order by count(*) desc
cat *-(12|13) | cut –f3 | sort | uniq -c > out
Hit <enter>?
1PB = 10^15 (1,000,000,000,000,000) bytes
1 PB = 231 days at 50MB/s
Solution: Massively Parallel Processing
#2
○●○○○
Big Data and Hadoop in the Cloud
HDFS
Reliable storage
MapReduce
Data analysis
Very large
log
(e.g TBs)
Very large
log
(e.g TBs)
Lots of actions
by John
Very large
log
(e.g TBs) Split into
small
pieces
Lots of actions
by John
Very large
log
(e.g TBs)
Process in a
hadoop cluster
Split into
small
pieces
Lots of actions
by John
Very large
log
(e.g TBs)
John’s
history
Process in a
hadoop cluster
Aggregate
the results
Split into
small
pieces
Lots of actions
by John
map
Input
file reduce
Output
file
Worker node
map
Input
file reduce
Output
file
map
Input
file reduce
Output
file
map
Input
file reduce
Output
file
Worker node
Worker node
Worker node
How
can we
help
John?
Very large
log
(e.g TBs)
Actionable Insight
Deploying a Hadoop Cluster is Hard
Big Data and Hadoop in the Cloud
#3
♥
○○●○○
We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.
Elastic On Demand
Pay as you go
Focus on
YOUR
business
Elastic On Demand
Pay as you go
Focus on
YOUR
business
November
Provisioned capacity
November
76%
24%
Provisioned capacity
November
November
On and Off Fast Growth
Variable Peaks Predictable Peaks
On and Off Fast Growth
Predictable PeaksVariable Peaks
WASTE
CUSTOMER DISSATISFACTION
Fast GrowthOn and Off
Predictable peaksVariable peaks
#4
○○○●○
EMR is Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Media/Advertising
Targeted
Advertising
Image and
Video
Processing
Oil & Gas
Seismic
Analysis
Retail
Recommendations
Transactions
Analysis
Life Sciences
Genome
Analysis
Financial Services
Monte Carlo
Simulations
Risk Analysis
Security
Anti-virus
Fraud Detection
Image
Recognition
Social
Network/Gaming
User
Demographics
Usage analysis
In-game metrics
0
1.000.000
2.000.000
3.000.000
4.000.000
5.000.000
6.000.000
Versions
1.0.3
0.20.205
0.20
0.18
Distributions
Apache Hadoop
Job Flows
Custom JAR
Cascading
Streaming
Ruby, Perl, Python, PHP, R, Bash, C++
Data Warehouse for Hadoop
SQL-like query language
Hive
High-level programming
Ideal for data flow / ETL
Pig
Near real time key/value
store for structured data
HBase
Distributed monitoring
of cluster and nodes
Ganglia
Statistical computing
and graphics
Machine learning library
discover Value in Data
Unknown Unknowns
Elastic On Demand
Pay as you go
Focus on
YOUR
business
Undifferentiated
Heavy Lifting
Focus on
YOUR
business
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
elastic-mapreduce
--create
--key-pair micro
--region eu-west-1
--name MyJobFlow
--num-instances 5
--instance-type m2.4xlarge
–-alive
--log-uri s3n://mybucket/EMR/log
Instance type/count
elastic-mapreduce
--create
--key-pair micro
--region eu-west-1
--name MyJobFlow
--num-instances 5
--instance-type m2.4xlarge
–-alive
--pig-interactive --pig-versions latest
--hive-interactive –-hive-versions latest
--hbase
--log-uri s3n://mybucket/EMR/log
Adding Hive, Pig and
Hbase to the job flow
Elastic On Demand
Pay as you go
Focus on
YOUR
business
1 instance for 1000 hours
=
1000 instances for 1 hour
Big Data and Hadoop in the Cloud
…to Thousands
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Turn Off the Resources and Stop Paying
Elastic On Demand
Pay as you go
Focus on
YOUR
business
Source: IDC Whitepaper, sponsored by Amazon, “The Business Value of Amazon Web Services Accelerates Over Time.” July 2012
70% lower 5 year TCO per app
AWS
On-
premises $3.01M
$0.90M
50% reduction in
analytics costs
Save more money by using Spot Instances
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
EMR with Spot Instances
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
EMR with Spot Instances
14 hrs
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
7 hrs
EMR with Spot Instances
With Spot
4 instances * 7 hrs * $0.50 = $14 +
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
EMR with Spot Instances
7 hrs
With Spot
4 instances * 7 hrs * $0.50 = $14 +
5 instances * 7 hrs * $0.25 = $8.75
Total = $22.75
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
EMR with Spot Instances
7 hrs
Time -50%
Cost -22%
With Spot
4 instances * 7 hrs * $0.50 = $14 +
5 instances * 7 hrs * $0.25 = $8.75
Total = $22.75
14 hrs
Without Spot
4 instances * 14 hrs * $0.50 = $28
EMR with Spot Instances
7 hrs
#5
○○○○●
“What kind of movies do people like ?”
More than 25 Million Streaming Members
50 Billion Events Per Day
30 Million plays every day
2 billion hours of video in 3
months
4 million ratings per day
3 million searches
Device location , time ,
day, week etc.
Social data
10 TB of streaming data per day
~1 PB of data stored in Amazon S3
S3
Wide range of processing languages used
EMR
Prod Cluster
(EMR)
S3
Data consumed in multiple ways
S3
EMR
Prod Cluster
(EMR)
Recommendation
Engine
Ad-hoc
Analysis Personalization
EMR
S3
EMR
EMR
Prod Cluster
(EMR)
Query Cluster
(EMR)
EMR
EMR
Big Data and Hadoop in the Cloud
Durability
Versioning
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Foursquare…
33 million users
1.3 million businesses
…generates a lot of Data
3.5 billion check-ins
15M+ venues,
Terabytes of log data
Uses EMR for
Evaluation of new features
Machine learning
Exploratory analysis
Daily customer usage reporting
Long-term trend analysis
Benefits of EMR
Ease-of-Use
“We have decreased the processing time for urgent data-analysis”
Flexibility
To deal with changing requirements & dynamically expand reporting clusters
Costs
“We have reduced our analytics costs by over 50%”
ApplicationStack
Scala/Liftweb API Machines WWW Machines Batch Jobs
Scala Application code
Mongo/Postgres/Flat
Files
Databases LogsDataStack
Amazon S3 Database Dumps Log Files
Hadoop Elastic Map Reduce
Hive/Ruby/Mahout Analytics Dashboard Map Reduce Jobs
mongoexport
postgres dump
Flume
ApplicationStack
Scala/Liftweb API Machines WWW Machines Batch Jobs
Scala Application code
Mongo/Postgres/Flat
Files
Databases LogsDataStack
Amazon S3 Database Dumps Log Files
Hadoop Elastic Map Reduce
Hive/Ruby/Mahout Analytics Dashboard Map Reduce Jobs
mongoexport
postgres dump
Flume
ApplicationStack
Scala/Liftweb API Machines WWW Machines Batch Jobs
Scala Application code
Mongo/Postgres/Flat
Files
Databases LogsDataStack
Amazon S3 Database Dumps Log Files
Hadoop Elastic Map Reduce
Hive/Ruby/Mahout Analytics Dashboard Map Reduce Jobs
mongoexport
postgres dump
Flume
ApplicationStack
Scala/Liftweb API Machines WWW Machines Batch Jobs
Scala Application code
Mongo/Postgres/Flat
Files
Databases LogsDataStack
Amazon S3 Database Dumps Log Files
Hadoop Elastic Map Reduce
Hive/Ruby/Mahout Analytics Dashboard Map Reduce Jobs
mongoexport
postgres dump
Flume
0
0,1
0,2
0,3
0,4
0,5
0,6
Female Male
Gender
0 10 20 30 40 50 60 70 80
Age
Gorilla Coffee
Gray's Papaya
Amorino
Thursday Friday Saturday Sunday
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Big Data and Hadoop in the Cloud
Python library
https://github.com/Yelp/mrjob
Log files
250 EMR clusters spun up
and down every week
Common Crawl
1000 Genomes Project
Census Data
54 other datasets
http://aws.amazon.com/publicdatasets/
Challenge:
Large amounts of computing resources
needed for short periods of time; significant
data storage costs
Solution:
Clusters of 100s of nodes on EMR running 4-5 hours
at a time
Leverages 1000 genomes Public Data Set on AWS —
free access to ~200 TB of genomes for over 2,600
people from 26 populations around the world.
Challenge:
Volatile weather is deadly to crops like grapes
Solution:
Built a predictive model based on freely
available data—
60 years of crop data,
14 TBs of soil data, and
1M government Doppler radar points
50 EMR clusters process new data as it comes
into S3 each day, continuously updating the
model.
150B Soil
Observations
3M Daily Weather
Measurements
850K Precision Rainfall
Grids Tracked
200 TB in Amazon S3
Big Data and AWS Cloud
Elastic and scalable
No upfront CapEx
Pay per use
+
+
On demand
+
=
Remove
constraints
Remove constraints = More experimentation
More experimentation = More innovation
Focus on your business
Leave undifferentiated heavy lifting to us
GRACIAS!
slideshare.net/AmazonWebServicesLATAM
http://aws.amazon.com/es/big-data/
José Papo
AWS Tech Evangelist
@josepapo

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