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Big Data

Tavo De León

BigDataArchitecture.com

New Frontiers for IT Management

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Executive Briefing

• Definition

• Catalysts

• Potential Value of Big Data

• Thought Leaers

• Leaing Technology Venors

• !ou " The #T Professional

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The Age of Big Data

“Data is a new class of economic asset,like currency and gold.”

Source: World Economic Forum 2012

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$hat is Big Data

%A massive volume of &oth structure an

unstructure ata that is so large that it's ifficultto (rocess )ith traitional ata&ase ansoft)are techni*ues.+

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$hat is Big Data

• $almart hanles more than , million customertransactions every hour.

• -ace&oo hanles /0 &illion (hotos from its user&ase.

• Decoing the human genome originally too ,0years to (rocess1 no) it can &e achieve in one)ee.

2 The Economist3 40,0

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Volume3 Velocity an Variety

Big ata s(ans three imensions

Big DataBig Data

VolumeVolumePetabytes per day/week   VelocityVelocity

Real-time captureand

Real-time analytics

VarietyVarietynstructured data,

web logs, audio,!ideo, image

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Catalyst " Commoity 5ervers

• Commoity server har)are creating the(ossi&ility for cost effective massively (arallel(rocessing 67PP8

 "

"#ample ser!er mig$t contain%

&P ' () &ores

R*+ ' ( erabyteDisk ' erabytes"t$ernet ' ( bit

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Catalyst " 9umans an the #nternet

• ,.4 Billion active mo&ile &roa&an su&scri(tions

• $e& sites )ith :00; million uni*ue visitors<month

 " -ace&oo

 " !ahoo

 " =oogle

 " !ouTu&e "

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Potential Value of Big Data

• >:00 &illion (otential annual value to ?5 healthcare.

• >@00 &illion (otential annual consumer sur(lusfrom using (ersonal location ata.

• @0 (otential in retailers o(erating margins.

Source: McKinsey Global Institute - 2010

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Thought Leaers

• The 7cinsey =lo&al #nstitute“Big data: The next frontier for innovation, competition, and productivity” 

 – May 2011

• =artner esearch

 " The timely conversion of ata to information is nextchallenge for cor(orations

 " ole of Data 5cientist

 

• eilly Boos

 " Eucation

 " 5trata Conference

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Leaing Technology Venors

Example Vendors

• #B7 " FeteGGa

• E7C " =reen(lum

• racle " Exaata

Commonality

• 7PP architectures

• Commoity 9ar)are

• DB75 &ase

• -ull 5HL com(liance

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9a(oo " (en 5ource

• 5tarte &y =oogle an !ahooI

• Fo) (en 5ource " 9aoo(

• %Fo5HL+ a((roach to ata

• -ounational TechnologiesJ " 9aoo( Data 5torage -rame)or

 " 7a(euce engine

 " 9#VE an P#= *uery tools•  Almost 5HL com(liant

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Leaing Venors 2 9aoo(

• Clouera " (en 5ource 9ADP " Prouction eleases

 " Very goo su((ort

 " Conferences an eucation "

•  AmaGon's Elastic Com(uting Clou " 7a(<euce environment

 " 7PP for everyone

 " Cost effective

 " An you can &uy a &ooI

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!ou " The #T Professional

• e*uire reaingJ

 " The 7cinsey re(ort

 " F!T 2 %The Age of Big Data+

• Big Data is an environment )hich re*uires care anfeeing

• Develo( in2house sills 6AmaGon EC48

• Proof of conce(t &efore (urchase

• Big Data analytic tools are %infants+

• Big Data &rings ne) (rivacy an com(lianceconcerns

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5taying Com(etitive

0or almost ( years baseball pitc$ers“producti!ity” $as been defined by t$eir"arned Run *!erage or ".R.*.

1t2s a statistic for pitc$ers t$at measures$ow good t$ey are at pre!enting runs.

E.R.A = Earned Runs/Innings Pitched x 9

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5taying Com(etitive 2 7oney&all

&orporations 3baseball teams4 needmore data and ad!anced analytics toremain competiti!e.

".R.* e!ol!ed into 5 6kill-1nteracti!e"arned Run *!erage

SIERA = 6.145 – 16.986*(SO/PA) +

11.434*(BB/PA) – 1.858*((GB-FB-

PU)/PA) + 7.653*((SO/PA)^2) +/–

6.664*(((GB-FB-PU)/PA)^2) +

1.13*(SO/PA)*((GB-FB-PU)/PA) –

5.195*(BB/PA)*((GB-FB-PU)/PA )

Source: New or! "imes

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