rapport veille salon-mobileit&bigdata

7/30/2019 Rapport Veille Salon-MobileIT&BigData

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EXIBITIONWATCHREPORT

MobileIT&BigData

23rd

25th

ofOctober,2012Paris,PortedeVersailles

Applicationsformobile,BusinessIntelligence


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VIEDOCForanyfurtherinformation:[email protected]

TABLEOFCONTENTS

ABSTRACT................................................................................................................................................................ 4

RESUME................................................................................................................................................................... 4

Part1. innovationsonmobileit........................................................................................................................ 5

1.1 BackgroundonGamificationonmobile................................................................................................ 51.1.1 Definitionofgamification.................................................................................................................. 5

1.1.2 Gamificationmarketforecast........................................................................................................... 6

1.1.3 Innovationsfromplatformproviders................................................................................................ 6

1.2 NomalysbyNomalys(Mobileapplication)............................................................................................ 7

1.3 TeopadbyThales................................................................................................................................... 8

Part2. Bigdata................................................................................................................................................ 12

2.1 BackgroundonBigData...................................................................................................................... 12

2.1.1 Definingbigdata............................................................................................................................. 12

2.1.2 CharacteristicsofBigData:ThefourVs.......................................................................................... 12

2.1.3 TheImportanceofBigData............................................................................................................ 13

2.1.4 EstimationsofITspendingdrivenbyBigDataissues..................................................................... 15

2.2

BigData

Architecture

Capabilities

and

their

primary

technologies

.....................................................

16

2.2.1 Comparisonofinformationarchitectures....................................................................................... 16

2.2.2 StorageandManagementCapability.............................................................................................. 17

2.2.3 DatabaseCapability......................................................................................................................... 19

2.2.4 ProcessingCapability....................................................................................................................... 20

2.2.5 DataIntegrationCapability............................................................................................................. 21

2.2.6 StatisticalAnalysisCapability.......................................................................................................... 22

2.3 Trendsonbigdata............................................................................................................................... 23

2.3.1 TheInternetofThingsalreadyhere................................................................................................ 24

2.3.2 Gettingtotherightbusinessmodel(s)fordata.............................................................................. 24

2.3.3 AddingaSociallayertotraditionalactivities.................................................................................. 24

2.3.4 TheNewFrontierofBusinessIntelligence&Semanticsatpetabytescale.....................................25

2.4

Keycompanies

in

the

Big

Data

exibition

in

Paris

.................................................................................

25

2.4.1 DataPublica.................................................................................................................................... 25

2.4.2 Altic................................................................................................................................................. 25

2.4.3 Talend.............................................................................................................................................. 26

Conclusion............................................................................................................................................................. 27

AboutVEILLESALON............................................................................................................................................. 28

PRESENTATIONofVIEDOCSARL........................................................................................................................... 29


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DISCLAIMER

Thisreport

was

compiled

from

interviews

conducted

by

us

with

the

exhibitors

present

at

each event, from gathering and analyzing information in conferences and from the

compilationofinformationonthewebafterwards.

Thus,thedatacontainedinthisreporthaveinformationvalue.Althoughtheobjectiveisto

disseminatetimelyandaccurate information,VEILLESALONcannotguaranteetheresult.

Anydamagethatmayresultfromuseofthisinformationcantbeimputedtothissite.The

useorreproductionofallorpartofthisdocumentisprohibitedwithoutthepriorwritten

consentof

VEILLE

SALON.

Forfulltermsandconditionsofuseofthisreport,thankyouforcontactingus.


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ABSTRACT

According to the organizers, the exhibition Mobile IT and Big Data have not attracted many visitors. Big

companieslikeOrange,SFR,Bouygues,FreefortelecomsorlikeIntel,Dell,IBMforBigDatawereabsent.But

theconferencesontheevolutionofthesesectorshavebeenverysuccessful. Inagloomyatmospherewhere

visitorsandexhibitorstalkopenlyabouttinybudgetsforinformationtechnology,somesectors,however,werequite healthy and innovative. This was the case for equipment manufacturers and developers of next

generationtelephony,orwebprovider.Thereweresome impressive innovations inthe fieldofsmartphones

coming from a large number of young companies, specializing in mobile business solutions. The advent of

smartphones and tablets is revolutionizing enterprise mobility. Judicious use of interfaces from the video

games industry brings playful applications, which allows more friendly use by customers. We talk about

"gamification"phenomenon,whichisabouttocommerciallyexplodeintheshortterm.

ConferencesonBigDatagrewquiteacrowdandallowedvisitorstodiscoveranemergingsectorthatshould

weighheavilyinthedevelopmentofenterprises.Inonly10years,theamountofdataincreasedexponentially.

Datastorageisacostlyproblemforbusinesses,butthesedataarerelativelyuntappedbycompanies.Theidea

of big data is to create added value from very diverse data. People now talk about flows, exchanges,

collaborationsratherthanstorage.Nothingissortedbuteverythingcanbefound.BigData(from10TBofdata)

isrevolutionizing

the

infrastructure

in

information

technology.

Environments

such

as

Hadoop

provide

flexibility

in resources and adapt to the workload by adding inexpensive servers in parallel. Big Data has generated a

turnoverof$17billionin2011anditisestimatedthatthisfigurewilldoubleby2016.Thegreatdebatewith

bigdataistofindabalancebetweendatatransparencyandprivacyofcitizens.

Keywords:mobile,gamification,smartphone,security,bigdata,datascientists,hadoop,businessintelligence

RESUME

Delaveummedesorganisateurs, lessalonMobileITetBigDatanontpasattirbeaucoupdevisiteursetles

grandsdu

milieu

comme

Orange,

SFR,

Bouygues,

Free

pour

les

tlcoms

ou

comme

Intel,

Dell,

IBM

pour

les

Big

Datataientabsents.Mais lesconfrencestechniquesetsocitalessur lvolutiondecessecteursontconnu

un vif succs. Dans une ambiance morose o visiteurs et exposants parlent ouvertement de chutes des

budgets aux technologies de linformation, certains secteurs affichent cependant une sant de fer, les

fabricantsdquipementsetdveloppeursde tlphoniedenouvellegnration,ou leshbergeurs,pourne

citerqueux.Onassisteparticulirement desinnovationsflorissantesdansledomainedessmartphonesavec

un grand nombre dejeunes socits, spcialises dans les solutions professionnelles mobiles. Larrive des

smartphones et des tablettes rvolutionne la mobilit en entreprise. Lutilisationjudicieuse des interfaces

venant de lindustrie desjeux vidos apporte un ct ludique aux applications, qui permet une meilleure

appropriation par les utilisateurs. On parle de gamification, phnomne amener exploser

commercialementtrscourtterme.

Lesconfrencessur leBigDataontamen lesvisiteursdcouvrirunsecteurnaissantquidevraitpesertrs

lourddans

le

dveloppement

des

entreprises.

On

assiste

depuis

10

ans

une

explosion

du

poids

des

donnes.

Le stockage de donnes est une problmatique couteuse pour les entreprises, mais ces donnes sont

relativementpeuexploitesparlesentreprises.Lidedesbigdataestdecrerdelavaleurajoutepartirdes

donnesdenaturetrsdiverses.Onraisonnedsormaisenflux,change,collaborationpluttquenstockage.

On ne classe rien mais on retrouve tout. Le Big Data ( partir de 10 To de donnes) est en train de

rvolutionner les infrastructuresdans les technologiesde linformation. LesenvironnementscommeHadoop

permettentdavoirunegrandesouplessedanslesressourcesetdesadapterlamassedetravailenajoutant

enparalllesdesserveurspeucouteux.LeBigDataadjgnrunchiffredaffairesde17milliardsdedollars

en2011etonestimequecechiffredoubleradici2016.Legranddbatavec lafinessedexploitation desbig

datavatreoplacerlecurseurentrelatransparencedesdonnesetlerespectdelavieprivedescitoyens.

Motscls:bigdata,stockage,donnes,valorisation,golocalisation, mobilit,smartphone,serveur,Hadoop


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PART1. INNOVATIONSONMOBILEIT

1.1 BACKGROUNDONGAMIFICATIONONMOBILE

1.1.1 Definitionof

gamification

Gamification istheuseofgamesorcompetitiontoencourageausertocompleteanactionorsetofactions.

Usersrespondtoarangeofpromptsandareencouragedtoreturnregularlytotheapplication.Theprompts

include:

Whatmakesgamificationsoattractive is the factthatwegenerallyenjoyactivelyparticipatingandengaging

with others through entertainment. It is in our human nature to interact and be entertained with playful

applications,particularlywhenthereareengaginggamedesignelementsemployed.

Consumergamesanddigitalentertainmentcontinuestoattractattentiongiventheinterestthepublichaswith

games.Compellinggamemechanicsanddesignareatthecoreofanengaginguserexperience.Gamification,

thereforemustworktoenhancetheuserexperienceinordertobetterengage,retain,motivateandpromote

overallparticipation.

Gamification takes advantage of game mechanics to deliver engaging applications, and make nongame


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applications more entertaining and appealing. By deploying these dynamics in a coordinated application, a

company can use games to motivate behaviours and drive outcomes for both the customer and the

organisation.

1.1.2 Gamificationmarketforecast

The adoption of applying game mechanics in more nontraditional industries has grown exponentially in the

past 18 months. This is due in part to the growth of social and mobile games, as well as the increasing

consumeradoptionofsocialmedia.

M2

Research

estimates

that

the

market

spend

on

gamification

solutions,

applying

game

mechanics

andbehavioralanalytics innontraditionalapplicationswillreach$242millionbytheendof2012,which ismore

thandoublefrom2011.Revenueestimatesarecomprisedofanumberofcomponentsthatincludes:

1. Platformvendorrevenue2. Agencyandproductionrevenue3. Internaldevelopment

1.1.3 Innovationsfromplatformproviders

2012isamilestoneyearforgamificationandasitgrowswillevolveintoaseriouscomponentofconsumerand

employee engagement. It will be critical for both platform providers as well as deploying organizations to

understand that implementing gamification is not a shortterm strategy. It is a longterm commitment that

requiresdiligence

in

audience

research,

application

design

and

activation/maintenance

to

ultimately

benefit

fromtheopportunitiesthatgamificationprinciplesoffer.


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Despite the anticipated growth rates, gamification will remain a market that will be carefully evaluated by

potentialcustomersforplatformproviders.MobileITtookreallyadvantageofgamificationforapplication,and

themaininnovationsdisplayedintheMobileITexhibitioninPariscomefromplatformproviders.

1.2 NOMALYSBYNOMALYS(MOBILEAPPLICATION)

Address:

46rueAugusteBlanqui

94250Gentilly,France

Tel:0146652158

Fax:0179735589

Contact:

CelineBLANC

Courriel:[email protected]

Website:http://www.nomalys.com/

NOMALYS offers the opportunity to nomad professionals using a Smartphone (iphone, iPad, Android,

BlackBerryetWindowsPhone8)tofinallyaccessthetotalityoftheirstrategiccompanysdata.

Source:Nomalys,2012Every

company

equipped

with

a

structured

IT

system

can

connect

it

to

the

Nomalys

application.

The

applications ergonomics, engine and algorithms have been designed to be generic, this means that every IT

systemcanbebrowsedbyanymobiledevicewiththesameergonomicsandcolorfuluserinterface.

However,NomalysisnotonlyawaytomakeyourCRMorERPmobile.Itisalsoachanceforeachcompanyto

buildthroughthepowerandthe innovativeergonomicsofNomalysanapplicationabletodisplaytheir large

range ofproductsand services. The access is immediate, intuitive,dynamic andsecured. It ispossible tobe

warnedinrealtimeofanyimportanteventhappeningonyourdatabase.


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Source:Nomalys,2012Connection

is

made

on

existing

CRM

or

ERP

software.

This

allows

to

access

data

such

as:

clients,

prospects,

stocks,invoices,quotations,pays,humanresources,complaints

Source:Nomalys,2012With the solution developed by NOMALYS, your software becomes mobile, dynamic, interactive and fully

promoted.

Nomalys received a Convergence 2012 awards in the Mobile IT exhibition. Nomalys has developed close

partnershipswithCNRS,InstitutTelecomfordevelopinguniquealgorithms.

1.3 TEOPADBYTHALES

Address:

ThalesCommunications&Security

45,ruedeVilliers

92200NeuillysurSeineCedex.

Website:http://www.thalesgroup.com

Contact:

RaphalBINET

ProductMarketingManager

Email:[email protected]

Tel:+33146132952

Mobile:+33608179391

TEOPAD isasecuringsolutionforprofessionalapplicationsonsmartphonesandtablets,developedbyThales

anddedicatedtocompaniesandpublicservices.

TEOPADallows

to

create

on

the

terminal

a

secureprofessional

environment

that

can

coexist

with

an

open

personal context. This professional environment is in the form of an application that can be started after a


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stronguserauthentication andbymeansofasimpleiconontheterminal'snativedesktop.Theusercanthen

accessaseconddesktop,whichconstituteshis/herprofessionalenvironment.Thelatteriscompletelyisolated

fromthepersonalandnativepartbyapatentedsandboxingtechnology.

Source:Thales,2012Thispartisentirelyencryptedandcontrolled,containsalltheapplications,dataandsettingsnecessaryforthe

userwithin

the

framework

of

his/her

business

activity:

Applications of all types: web browser, email client, viewers, note pads, telephony client, businessapplications,etc.

Documents,contactdatabase,personalorganizer,emailarchives,etc.TheinnovationsdevelopedbyThalesenableTEOPADtoproposesignificantdifferentiatorswithrespecttothe

othermarketsolutions:

Flexibility in choosing the terminal: for a given OS, the solution may be deployed on most of themarketterminalsusingthisOS.

Flexibilityinchoosingtheapplications: foragivenOS,mostoftheapplicationsavailableonthemarketmaybehostedandprotectedinthesecureenvironment.Thisappliestonativeapplications,aswellas

tothirdapplicationsorapplicationsdevelopedbythecompanyforitsownneeds.

Protection of the information in all its forms: information remains vulnerable when manipulated, transmittedorstored.Therefore,thereisnouseencryptingonlyemailsortelephony,asmostofthecurrent solutions offer to do so. TEOPAD allows to protect information in all its editing, viewing or

exchangingcontexts.

Flexibilityofthesecureperimeter:thanksto"TEOPADMarketPlace"thecompanycanmakeanytimenewsecureapplicationsavailableforitsemployees.Forinstance,theycanbeadapteddependingon

theemployees'missionsorbusinesstrips.Thisflexibilityenablestheemployeetotravel incomplete

safetywithaterminal,thecontentofwhichisstrictlyadaptedtohis/herneeds.He/shecanleavewith

a terminal with no professional context, the latter being downloaded securely once he/she has

reachedhis/herdestination.

Simplicity of deployment for the user: once he/she has received his/her authentication means, theuser downloads the TEOPAD application and his/her customized professional context from the

"TEOPADMarket

Place"

available

on

the

Intranet

of

his/her

company.

Userfriendly interface: TEOPAD preserves integrally the ergonomics of the native OS and theapplicationsused.


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No additional specific infrastructure: TEOPAD is connected very simply to the existing informationsystem.Thereisnousedeployingproprietaryserversorgateways,whichhighlylimitsthecosts.

Offerofhighqualityprofessionalservicesdedicatedtotheusers Flexibleoperation:itmaybepartiallyorcompletelygiventoatrustworthythird.

Source:Thales,2012TheTeopadsandboxingtechnologyisauniqueandpatentedtechnologythatallowstocreateterminalduality

between

two

environments

professional

and

personal

working

simultaneously,

but

independently,and

withoutresortingtoproprietaryapplications.

This technology does not rely on virtualization principles, which makes it particularly light, with all possible

benefits intermsofperformanceandautonomy.TheAndroidapplicationsareauthorizedtoperformspecific

tasksorreachsystemcomponentsdependingontheprivilegestheyreceived.

TheTEOPADSANDBOXsystemcontrolstheauthorizations, andthen,filterstheexchangesbetween:

professionalandpersonalapplications; professionalapplicationsandoperatingsystem.

ThismechanismallowstheInformationSystemDepartmenttolimittheinteractioncapabilitiesofprofessional

applications

with

their

environment.

The

ringfenced

professional

environment

is

then

generated

and

is

displayedintheformofaseparatedesktopontheterminal.


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This technology supplies efficient means to fight against intrusions, information leaks or trapping of

professionalapplications.

TheTEOPADSANDBOXadvantages:

customizedcompartmentalization ofprofessionalapplicationsanddatawithrespecttotherestoftheterminal;

professionaldesktopthatcanhostanytypeofapplicationsavailableonthemarketordevelopedbythe

company

(no

mandatory

Thales

proprietary

application);

simultaneousoperationofprofessional andpersonalenvironmentswithuniquenotificationinterfacefortheuser(Androidnativebar);

applicationcontentexclusivelyfromthecompany'sTeopadMarketPlaceandentirelyundercontrolofthelatter;

protectionofprofessionaldata,includingthosebeingvisualized,whentheyarenolongerencrypted; verypoorprintontheterminal,whichenablestomaintainperfectlytheperformanceofthelatter; userfriendlyinterfacemaintained.

The TEOPAD SANDBOX compartmentalization service is proposed independently from the local encryption

serviceontheterminal.Thesearetwocomplementaryservices.

Source:Thales,2012TheTEOPADsolutioniscomposedofthefollowingelements:

Fortheuser:o TheTEOPADapplicationtobeinstalledontheterminal.o TheTEOPADMarketPlaceclientapplication.

Forthecompany:o TheTEOPADinfrastructureisparticularlylightasitdoesnotrequireanyproprietaryelement

toconnecttheuserstotheinformationsystem.

o It allows a centralized and industrialized deployment, and then operation of TEOPAD. Thetoolsenableinparticulartocreategenericorcustomizedprofilesandtobecomeadaptedto

fleetswithhighdimensionsorspecializedperbusinessactivity.


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PART2. BIGDATA

2.1 BACKGROUNDONBIGDATA

2.1.1 Definingbig

data

Bigdatatypicallyreferstothefollowingtypesofdata:

Traditional enterprise data includes customer information from CRM systems, transactional ERPdata,webstoretransactions,generalledgerdata.

Machinegenerated /sensor data includes Call Detail Records (CDR), weblogs, smart meters,manufacturingsensors,equipmentlogs(oftenreferredtoasdigitalexhaust),tradingsystemsdata.

Social data includes customer feedback streams, microblogging sites like Twitter, social mediaplatformslikeFacebook

TheMcKinseyGlobalInstituteestimatesthatdatavolumeisgrowing40%peryear,andwillgrow44xbetween

2009and2020.Butwhile itsoftenthemostvisibleparameter,volumeofdata isnottheonlycharacteristic

thatmatters.

BigDataissizedinpeta,exa,andsoonperhaps,zettabytes!And,itsnotjustaboutvolume,theapproachto

analysiscontendswithdatacontentandstructurethatcannotbeanticipatedorpredicted.Theseanalyticsand

the science behind them filter low value or lowdensity data to reveal high value or highdensity data. As a

result,new

and

often

proprietary

analytical

techniques

are

required.

Big

Data

has

abroad

array

of

interesting

architecturechallenges.

2.1.2 CharacteristicsofBigData:ThefourVs

Infact,therearefourkeycharacteristicsthatdefinebigdata:Volume,Velocity,Varietyand Value.Itisoften

said thatdata volume, velocity, and variety define Big Data, but the unique characteristic of Big Data is the

mannerinwhichthevalueisdiscovered.

a) Volume.Machinegenerateddataisproducedinmuchlargerquantitiesthannontraditionaldata.Forinstance,asingle

jetenginecangenerate10TBofdata in30minutes.Withmore than25,000airline flightsperday, thedailyvolumeofjustthissingledatasourceruns into thePetabytes.Smartmetersandheavy industrialequipment


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likeoilrefineriesanddrillingrigsgeneratesimilardatavolumes,compoundingtheproblem.Peoplereallyspeak

aboutbigdatawhenthevolumeisabove10To.

b) Velocity.Social media data streams while not as massive as machinegenerated data produce a large influx of

opinions

and

relationships

valuable

to

customer

relationship

management.

Even

at

140

characters

per

tweet,

thehighvelocity(orfrequency)ofTwitterdataensureslargevolumes(over8TBperday).

c) Variety.Traditional data formats tend to be relatively well described and change slowly. In contrast, nontraditional

data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new

marketingcampaignsexecuted,newdatatypesareneededtocapturetheresultantinformation.

d) ValueTheeconomicvalueofdifferentdatavariessignificantly.Typicallythereisgoodinformationhiddenamongsta

larger body of nontraditional data; the challenge is identifying what is valuable and then transforming and

extracting

that

data

for

analysis.

With Big Data, the value is discovered through a refining modeling process: make a hypothesis, create

statistical, visual, or semantic models, validate, then make a new hypothesis. It either takes a person

interpretingvisualizations ormakinginteractiveknowledgebasedqueries,orbydevelopingmachinelearning

adaptivealgorithmsthatcandiscovermeaning.Andintheend,thealgorithmmaybeshortlived.

2.1.3 TheImportanceofBigData

Thegrowthofbigdataisaresultoftheincreasingchannelsandvarietyofdataintodaysworld.Someofthe

new data sources are usergenerated content through social media, web and software logs, cameras,

informationsensingmobiledevices,aerialsensorytechnologies,genomics,andmedicalrecords.

Source:Cisco,VNIServiceAdoptionForecast,20112016,May2012Companieshaverealizedthatthere iscompetitiveadvantage inthis informationandthatnow isthetimeto


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putthisdatatowork.Tomakethemostofbigdata,enterprisesmustevolvetheirITinfrastructurestohandle

therapidrateofdeliveryofextremevolumesofdata,withvaryingdatatypes,whichcanthenbe integrated

withanorganizationsotherenterprisedatatobeanalyzed.

Whenbigdataisdistilledandanalyzedincombinationwithtraditionalenterprisedata,enterprisescandevelopa more thorough and insightful understanding of their business, which can lead to enhanced productivity, a

strongercompetitivepositionandgreaterinnovationallofwhichcanhaveasignificantimpactonthebottom

line.

For example, in the delivery of healthcare services, management of chronic or longterm conditions is

expensive.Useofinhomemonitoringdevicestomeasurevitalsigns,andmonitorprogressisjustonewaythat

sensordatacanbeusedtoimprovepatienthealthandreducebothofficevisitsandhospitaladmittance.

Manufacturingcompaniesdeploysensorsintheirproductstoreturnastreamoftelemetry.Sometimesthisis

used to deliver services like OnStar, that delivers communications, security and navigation services. Perhaps

moreimportantly,thistelemetryalsorevealsusagepatterns,failureratesandotheropportunitiesforproduct

improvementthat

can

reduce

development

and

assembly

costs.

TheproliferationofsmartphonesandotherGPSdevicesoffersadvertisersanopportunitytotargetconsumers

when they are in close proximity to a store, a coffee shop or a restaurant. This opens up new revenue for

serviceprovidersandoffersmanybusinessesachancetotargetnewcustomers.

Retailersusuallyknowwhobuystheirproducts.Useofsocialmediaandweb logfilesfromtheirecommerce

sitescanhelpthemunderstandwhodidntbuyandwhytheychosenotto,informationnotavailabletothem

today.Thiscanenablemuchmoreeffectivemicrocustomersegmentationandtargetedmarketingcampaigns,

aswellasimprovesupplychainefficiencies.

Finally, social media sites like Facebook and LinkedIn simply wouldnt exist without big data. Their business

modelrequires

apersonalized

experience

on

the

web,

which

can

only

be

delivered

by

capturing

and

using

all

theavailabledataaboutauserormember.


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2.1.4 EstimationsofITspendingdrivenbyBigDataissues

Thehugevolumesofdatageneratedbytodaysdigitalbusinesses,knownasbigdata,willdrive$28billionof

worldwide IT spending this year and $34bn next year, according to a forecast from Gartner, the IT research

firm.

At the same time, Gartner predicted that by 2015, 4.4 million ITjobs will be created to support big data,

including 1.9 million in the US, but warned that there will be a scramble for the limited number of IT

professionalsqualifiedtofillthesejobs.


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$232Billionisprojectedtobesoldintotalacrossallcategoriesintheforecastfrom2011to2016.From$24.4

Billionin2011to$43.7Billionin2016,thispresentsa12.42%CAGRintotalmarketgrowth.

2.2 BIGDATAARCHITECTURECAPABILITIESANDTHEIRPRIMARYTECHNOLOGIES

2.2.1 Comparisonofinformationarchitectures

Big data differs from other data realms in many dimensions. In the following table you can compare and

contrastthecharacteristicsofbigdataalongsidetheotherdatarealms.

Source:Oracle,2012These different characteristics have influenced how you capture, store, process, retrieve, and secure your

information architectures. As you evolve into Big Data, you can minimize your architecture risk by finding

synergies across your investments allowing you to leverage your specialized organizations and their skills,

equipment,standards,

and

governance

processes.


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Hereisanexamplefordataflowarchitecturediagramwhenbigdataisusedforcombinedanalytics.

Source:Oracle,2012

2.2.2 StorageandManagementCapability

a) HadoopDistributedFileSystem(HDFS)

HDFShastwomainlayers:

Namespaceo Consistsofdirectories,filesandblockso

It

supports

all

the

namespace

related

file

system

operations

such

as

create,

delete,

modify

andlistfilesanddirectories.

BlockStorageServicehastwoparts


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o BlockManagement(whichisdoneinNamenode) Providesdatanodeclustermembershipbyhandlingregistrations, andperiodicheart

beats.

Processesblockreportsandmaintainslocationofblocks. Supports block related operations such as create, delete, modify and get block

location.

Managesreplicaplacementandreplicationofablockforunderreplicatedblocksanddeletesblocksthatareoverreplicated.

o Storage is provided by datanodes by storing blocks on the local file system and allowsread/writeaccess.

In order to scale the name service horizontally, federation uses multiple independent

Namenodes/namespaces. TheNamenodesarefederated,that is,theNamenodesare independentanddont

require coordination with each other. The datanodes are used as common storage for blocks by all the

Namenodes. Each datanode registers with all the Namenodes in the cluster. Datanodes send periodic

heartbeatsandblockreportsandhandlescommandsfromtheNamenodes.HeretheKeyBenefits

Namespace Scalability HDFS clusterstorage scales horizontally but thenamespacedoes not. Largedeployments or deployments using lot of small files benefit from scaling the namespace by adding

moreNamenodes

to

the

cluster

Performance File system operation throughput is limited by a single Namenode in the priorarchitecture. Adding more Namenodes to the cluster scales the file system read/write operations

throughput.

Isolation A single Namenode offers no isolation in multi user environment. An experimentalapplicationcanoverloadtheNamenodeandslowdownproductioncriticalapplications.Withmultiple

Namenodes,differentcategoriesofapplicationsanduserscanbeisolatedtodifferentnamespaces.

Bywayofconclusion,herearethemaincharacteristicsofHDFSknownbydevelopers:

AnApacheopensourcedistributedfilesystem,http://hadoop.apache.org Expectedtorunonhighperformancecommodityhardware Known for highly scalable storage and automatic data replication across three nodes for fault

tolerance Automaticdatareplicationacrossthreenodeseliminatesneedforbackup

Writeonce,readmanytimesb) ClouderaManager:

Cloudera Manager is the marketleading management platform for CDH (Cloudera's Distribution, including

Apache Hadoop). As the industrys first endtoend management application for Apache Hadoop, Cloudera

Manager sets the standard for enterprise deployment by delivering granular visibility into and control over

every part of CDH empowering operators to improve cluster performance, enhance quality of service,

increasecomplianceandreduceadministrativecosts.

HerearethemaincharacteristicsofClourderaManager:

Cloudera Manager is an endtoend management application for Clouderas Distribution of ApacheHadoop,http://www.cloudera.com

Cloudera Manager gives a clusterwide, realtime view of nodes and services running; provides asingle,

central

place

to

enact

configuration

changes

across

the

cluster;

and

incorporates

afull

range

of

reportinganddiagnostictoolstohelpoptimizeclusterperformanceandutilization.


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2.2.3 DatabaseCapability

a) OracleNoSQLOracleNoSQLDatabasedeliversscalablethroughputwithboundedlatency,easyadministration, andasimple

programming model. It scales horizontally to hundreds of nodes with high availability and transparent load

balancing."NoSQL"

is

ageneral

term

meaning

that

the

database

isn't

an

RDBMS

which

supports

SQL

as

its

primaryaccess language,buttherearemanytypesofNoSQLdatabases:BerkeleyDB isanexampleofa local

NoSQLdatabase,whereasHBaseisverymuchadistributeddatabase.

Source:Oracle,2012HerearethemaincharacteristicsofOracleNoSQL:

Dynamicandflexibleschemadesign.Highperformancekeyvaluepairdatabase.Keyvaluepair isanalternativetoapredefinedschema.Usedfornonpredictiveanddynamicdata.

Able to efficiently process data without a row and column structure. Major + Minor key paradigmallowsmultiplerecordreadsinasingleAPIcall

Highlyscalablemultinode,multipledatacenter,faulttolerant,ACIDoperations Simpleprogrammingmodel,randomindexreadsandwrites Not Only SQL. Simple pattern queries and customdeveloped solutions to access data such as Java

APIs.

b) ApacheHBaseApache HBase is the Hadoop database, a distributed, scalable, big data store. You can use Apache HBase

whenyouneedrandom,realtimeread/writeaccesstoyourBigData.Thisproject'sgoalisthehostingofvery

largetables billionsofrowsXmillionsofcolumns atopclustersofcommodityhardware.ApacheHBaseis

an

open

source,

distributed,

versioned,

column

oriented

store

modeled

after

Google's

Bigtable:

A

Distributed

Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage

provided by the Google File System, Apache HBase provides Bigtablelike capabilities on top of Hadoop and

HDFS.

HerearethemaincharacteristicsofApacheHbase:

Allowsrandom,realtimeread/writeaccess Strictlyconsistentreadsandwrites Automaticandconfigurableshardingoftables AutomaticfailoversupportbetweenRegionServers

c) ApacheCassandraThe Apache Cassandra database is the right choice when you need scalability and high availability without


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compromising performance. Linear scalability and proven faulttolerance on commodity hardware or cloud

infrastructuremakeittheperfectplatformformissioncriticaldata.

HerearethemaincharacteristicsofApacheCassandra:

Data model offers column indexes with the performance of logstructured updates, materializedviews,andbuiltincaching

Faulttolerancecapabilityisdesignedforeverynode,replicatingacrossmultipledatacenters Canchoosebetweensynchronousorasynchronousreplicationforeachupdate

d) ApacheHiveHiveisadatawarehousesystemforHadoopthatfacilitateseasydatasummarization,adhocqueries,andthe

analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project

structure onto this data and query the data using a SQLlike language called HiveQL. At the same time this

languagealsoallowstraditionalmap/reduceprogrammerstoplugintheircustommappersandreducerswhen

itisinconvenientorinefficienttoexpressthislogicinHiveQL.

Hive isbasedonHadoop,which isabatchprocessingsystem.Asaresult,Hivedoesnotandcannotpromise

lowlatenciesonqueries.Theparadigmhereisstrictlyofsubmittingjobsandbeingnotifiedwhenthejobsare

completedasopposedtorealtimequeries.IncontrasttothesystemssuchasOraclewhereanalysisisrunona

significantlysmalleramountofdata,buttheanalysisproceedsmuchmoreiterativelywiththeresponsetimes

betweeniterationsbeing lessthanafewminutes,Hivequeriesresponsetimesforeventhesmallestjobscan

beoftheorderofseveralminutes.Howeverforlargerjobs(e.g.,jobsprocessingterabytesofdata)ingeneral

theymayrunintohours.

Insummary,lowlatencyperformanceisnotthetoppriorityofHive'sdesignprinciples.WhatHivevaluesmost

are scalability (scale out with more machines added dynamically to the Hadoop cluster), extensibility (with

MapReduceframeworkandUDF/UDAF/UDTF),faulttolerance,andloosecouplingwithitsinputformats.

HerearethemaincharacteristicsofHive:

Toolstoenableeasydataextract/transform/load (ETL)fromfilesstoredeitherdirectlyinApacheHDFSor

in

other

data

storage

systems

such

as

Apache

HBase

UsesasimpleSQLlikequerylanguagecalledHiveQL QueryexecutionviaMapReduce

2.2.4 ProcessingCapability

a) MapReduce

Source:Oracle,2012


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MapReduce is a programming model and an associated implementation for processing and generating large

data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate

key/value pairs, and a reduce function that merges all intermediate values associated with the same

intermediatekey.Manyrealworldtasksareexpressibleinthismodel.

HerearethemaincharacteristicsofMapReduce:

DefinedbyGooglein2004 Breakproblemupintosmallersubproblems Abletodistributedataworkloadsacrossthousandsofnodes CanbeexposedviaSQLandinSQLbasedBItools

b) ApacheHadoop

ApacheHadoopis100%opensource,andpioneeredafundamentallynewwayofstoringandprocessingdata.

Insteadofrelyingonexpensive,proprietaryhardwareanddifferentsystemstostoreandprocessdata,Hadoop

enablesdistributedparallelprocessingofhugeamountsofdataacrossinexpensive,industrystandardservers

thatboth store and process thedata, and can scale without limits. With Hadoop, no data is too big.And in

todayshyperconnectedworldwheremoreandmoredataisbeingcreatedeveryday,Hadoopsbreakthrough

advantagesmeanthatbusinessesandorganizationscannowfindvalue indatathatwasrecentlyconsidered

useless.

Hereare

the

main

characteristics

of

Apache

Hadoop:

LeadingMapReduceimplementation Highlyscalableparallelbatchprocessing Highlycustomizableinfrastructure Writesmultiplecopiesacrossclusterforfaulttolerance

2.2.5 DataIntegrationCapability

a) OracleBigDataConnectors,OracleLoaderforHadoop,OracleDataIntegratorBuiltfromthegroundupbyOracle,OracleBigDataConnectorsdeliversahighperformanceHadooptoOracle

Database integration solution and enables optimized analysis using Oracles distribution of open source R

analysisdirectlyonHadoopdata.Byprovidingefficientconnectivity,BigDataConnectorsenablesanalysisofall

dataintheenterprisebothstructuredandunstructured.


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HerearethemaincharacteristicsofBigdataconnectors:

ExportsMapReduceresultstoRDBMS,Hadoop,andothertargets ConnectsHadooptorelationaldatabasesforSQLprocessing Includes a graphical user interface integration designer that generates Hive scripts to move and

transformMapReduceresults

Optimizedprocessingwithparalleldataimport/export CanbeinstalledonOracleBigDataApplianceoronagenericHadoopcluster

2.2.6 StatisticalAnalysisCapability

a)Open

Source

Project

R

and

Oracle

R

Enterprise:


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Risafreesoftwareenvironmentforstatisticalcomputingandgraphics.Itcompilesandrunsonawidevariety

of UNIX platforms, Windows and MacOS. R provides a wide variety of statistical (linear and nonlinear

modelling,classicalstatisticaltests,timeseriesanalysis,classification, clustering,...)andgraphicaltechniques,

and ishighlyextensible.TheS language isoftenthevehicleofchoiceforresearch instatisticalmethodology,

andRprovidesanOpenSourceroutetoparticipationinthatactivity.

Oneof

R's

strengths

is

the

ease

with

which

well

designed

publication

quality

plots

can

be

produced,

including

mathematicalsymbolsandformulaewhereneeded.Greatcarehasbeentakenoverthedefaultsfortheminor

designchoicesingraphics,buttheuserretainsfullcontrol.RisavailableasFreeSoftwareunderthetermsof

the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a

widevarietyofUNIXplatformsandsimilarsystems(includingFreeBSDandLinux),WindowsandMacOS.

HerearethemaincharacteristicsofprojectR:

Programminglanguageforstatisticalanalysis Introduced into Oracle Database as a SQL extension to perform high performance indatabase

statisticalanalysis

OracleREnterpriseallowsreuseofpreexistingRscriptswithnomodification

2.3 TRENDSONBIGDATA

IntheBigDataexhibitioninParis,innovationswerenotreallydisplayedastheBigDataworldiscontinuously

evolvingtowardssomething,noonereallyknows.So,expertsweremostlyexchangingwordsonwhattheyare

doing and most importantly on how they feel about the future on big data. They were all agreeing on one

thing:BigDataissomethingthatisgoingtofuelthe21stcenturyanditisalmostimpossibletoforecasthowbig

aneconomicalimpactwillcomefromtheuseofBigData.

Indeed,anewkindofjobiscoming:Datascientist!But,alltheexpertspointedoutthattherewillbeashortage

of talent for thesejobs. The advance of big data shows no signs of slowing. Data scientists are


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difficult and expensive to hire, and given the very competitive market for their services, difficult to retain.

There simplyare not a lotof peoplewith their combinationof scientificbackgroundandcomputational and

analyticalskills.

Amongtheconferences, itwaspossibletodefinesometrendsintheBigDataworld.

2.3.1 TheInternetofThingsalreadyhere

ItisnotsolongagothattheInternetofThings(avastcollectionofsmalldevicesseamlesslyconnectedtothe

Net)wasstilljustaconceptinresearchpapers.Andbeforeyouknowit,itshere,andlikeMonsieurJourdain,

peopledontquitefullyunderstand it.Even ifyouthinkcallingyoursmartphoneaThing isdebatable,and

yetitisaThingthatsendslotsandlotsofinformationtomanyserversworldwide,youwouldbeamazedto

knowthenumberofanonymousdevicesthatarealreadyfullyconnected.

Forexample,LaPostehasworkedwithExaleadonconnectingtheoptoelectronicmachinesthatitusestofilter

and sort our mail to the Net. It then uses all the information gathered to build a fullfledged business

intelligencetool,usedtooperationallymonitorthesystem.Anotherexample:didyouknowthathighendcar

manufacturers

have

turned

their

vehicles

into

Things

that

keep

sending

monitoring

information

to

central

serverstoassurebetterserviceandmaintenance?OnehastounderstandthateverysuchThingcreateshuge

logsof,literally,hundredsofbillionsofrecords:thatsmorethanpagesontheentireWeb!

2.3.2 Gettingtotherightbusinessmodel(s)fordata

Data,isthenewfrontierthesedays.BigData,OpenData,DaaS(DataasaService),asonecannameit.Datais

likeSoftware, it isveryscalable:one investsheavily tocreatedatasets,andthensellsthemby themillions,

withzeroorverysmallmarginalcosts.Atleastthatishowthetheorygoes.

But infairness, itshardtosaythatanybodyhascrackedtherightbusinessmodelfordata.For instance,one

interesting question remains: to be scalable, a data set needs to be reusable by many applications and

developers.But

then,

the

value

of

such

adata

data

set

is

probably

very

low,

unless

its

absolutely

needed

to

build everybodys application andyouhaveexclusivity,which is likely to beaveryrare case,especiallywith

OpenData.

Attheotherendofthespectrum,usingtheBigDataartillerytobuildaveryspecificdatasetcanyieldavery

exclusiveproductthatcanonlybeusedbyoneormaybeahandfulofnoncompetingcompanies.Suchadata

setcanbeveryexpensive(tobuildandtobuy),andcanalsocreatealotofvalueforthecompanythatusesit.

Butitsanentirelydifferentbusinessmodelthatisverydifferentfromtheintrinsically scalablebusinessmodel

ofthesoftwareindustry(especially, SaaS).Atleastuntilsomeonecracksit.

2.3.3 AddingaSociallayertotraditionalactivities

Well,thatisalsoaveryinterestingtrend:usingsocialnetworkslikeTwittertoproducerealtimevoiceofthe

customerapplications. Indeed,Facebookknowswhatyouaredoing,Twitterknowswhatyouaresaying,and

Googleknowswhatyouarethinking.

Forinstance,MesagraphisworkingwithbroadcasterstobuildiPadapplicationsconnectedtoTVprogramsso

thatyoucancommentandinteractwithotherviewersinrealtime,whileyourewatchingashow.Thatistruly

revolutionary:finally,awaytoconnectbacktothebroadcasters.Consumerscanfindtheirinterestshere,quite

obviously,butatthesametime,thinkoftheimplicationsintermsofadvertising.Realtimeadvertising,even.

Finegrainedaudiencesegmentation.Thisisanentirelynewfieldwithallsortsofpromisesandchallenges.

Another very interesting application that was presented at WWW2012 is the use of tweets to monitor the

Netflixmediastreamingservice,bydetectingtweetscontainingphraseslikeisout(comeon,guys,youcan

dobetterthanthat:).Evenwithverysimpleheuristics,about90%ofoutageswerecorrectlydetected.


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2.3.4 TheNewFrontierofBusinessIntelligence&Semanticsatpetabytescale

The Internet of Things ismakingpetabytescales a reality today (apetabyte is1,000 terabytes,or1,000,000

gigabytes).Acopyof theentireWebamounts to several petabytes. SoBigData technologiesareneeded to

handlesuchavastamountofdata,andonehastoperformsomeformofBusinessIntelligencetomakesense

ofit.

Therearetwomajorbreakthroughstohandlethischallenge.

Ononeside,RAMbaseddatabases,wheredataisorganizedincolumns,asopposedtorows,allowforvery

fastprocessingoflargequantitiesofdata(aslongasthisdatafitsinRAM,thatis).Slicinganddicingcouldntbe

anyfasteroreasier.

Ontheotherhand,searchengines,whicharecolumnarbyessence,areevolvingtohandlemanymorekind

ofdata(semantic,numeric,etc.),arebecomingmoreandmoretransactional(ACID,inbarbarianterms)and

canprocessevenlargerdatasetssincetheydonotrequirethatentiredatasetsfitinRAM.

You get to choose your favorite. But one thing is clear: semantic treatment of textual data will be a major

requirement

for

next

generation

Business

Intelligence

platforms.

That

is

the

next

frontier

for

Big

Data.

And

searchenginesareuniquelypositionedtowinthisrace.

2.4 KEYCOMPANIESINTHEBIGDATAEXIBITIONINPARIS

2.4.1 DataPublica

Address:

DataPublica 8rueJouffroydAbbans

75017Paris,France

Website:http://www.datapublica.com/

Contact:

M.FranoisBANCILHON

Mail:francois.bancilhon@data publica.com

Created in July 2011, Data Publica is one of the leading historical open data in France. The company has

benefitedfromtechnological investmentsmade in2010aspartofaR&DprojectThecompanywas initially

fundedbyagroupof"angels"andtheseedfundITTranslation.

DataPublicaisacompanyworkingonassemblingdatasetsbuiltfrombothpublicdataandopendata,andthen

sellingthesedatasetstocompaniestohelpthembuildinnovativeapplications.DataPublicadescribesitselfas

a Data Vendor similar, in the domain of Open Data, to what Software Vendors are to the domain of

Software.

2.4.2 Altic

Address:

95AvenueVictorHugo,93360NEUILLYPLAISANCE

Tel:0953646369

Website:http://www.altic.org/

Contact:MarcSALLIERES(CEO),[email protected]

ALTICisanALTernativeofInformationandCommunication.

ItisanOpenSourceSoftwareintegratorcreatedinJuneof2004,andafoundingmemberoftheASS2L.ALTIC

assistscompaniesandadministrationtoimplementthemanagementsoftwareinOpenSource.Itworksonthe

following domains and open source solutions: Business Solutions (SpagoBI, Talend, JasperReports, BIRT,

LemonOLAP),

Management

Solutions

(Compiere,

Vtiger,

SQL/Ledger),

Communication

Solutions

(Joomla!,

Tutos,LemonLDAP).AlticsupportsalsotheLemonLDAPproject,theOpenSourceWebSSO.


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2.4.3 Talend

Address:

TalendSA,9ruePags,

92150

SuresnesFranceWebsite:http://fr.talend.com/

Contact:

M.CdricCARBONE

Tel:+33

146

25

06

00

[email protected]

Talend is one of the largest pure play vendors of open source software, offering a breadth of middleware

solutionsthataddressbothdatamanagementandapplicationintegrationneeds.

Sincetheemergenceofdataintegrationanddataqualitytoolsinthe1990s,andthemorerecentappearance

of Master Data Management solutions, the data management market has been dominated by asmall and

quickly consolidating number of traditional vendors offering proprietary, closed solutions, which only the

largest and wealthiest organizations can afford. The situation in the application integration space is quite

similar, with significant consolidation occurring as well. As a result, only a minority of organizations use

commercial solutions to meet their data management and application integration needs. Indeed, these

solutions

not

only

demand

a

steep

initial

investment,

but

they

also

often

require

significant

resources

to

manageimplementationandongoingoperation.

Furthermore,companiesarefacedwithexponentialgrowth inthevolumeandheterogeneityofthedataand

applicationstheyneedtomanageandcontrol.AkeychallengethatITdepartmentsfacetodayisensuringthe

consistency of their data and processes by using modeling tools, workflow management and storage, the

foundationsofdatagovernanceinanycompanytoday.Thischallenge isactuallyfacedbyorganizationsofall

sizes notonlythelargestcorporations.

Injustafewyears,Talendhasbecometherecognizedmarket leader inopensourcedatamanagement.The

acquisition in2010ofSopera,aleader inopensourceapplicationintegration, hasreinforcedTalendsmarket

coverage,creatingagloballeaderinopensourcemiddleware. Manylargeorganizationsaroundtheglobeuse

Talend's products and services to optimize the costs of data integration, data quality, Master Data

Management (MDM) and application integration. With an ever growing number of product downloads andpayingcustomers,Talendoffersthemostwidelyusedanddeployeddatamanagementsolutionsintheworld.


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CONCLUSION

According to the organizers, the exhibition Mobile IT and Big Data have not attracted many visitors. Big

companieslikeOrange,SFR,Bouygues,FreefortelecomsorlikeIntel,Dell,IBMforBigDatawereabsent.But

theconferencesontheevolutionofthesesectorshavebeenverysuccessful. Inagloomyatmospherewhere

visitorsandexhibitorstalkopenlyabouttinybudgetsforinformationtechnology,somesectors,however,werequite healthy and innovative. This was the case for equipment manufacturers and developers of next

generationtelephony,orwebprovider.Thereweresome impressive innovations inthe fieldofsmartphones

coming from a large number of young companies, specializing in mobile business solutions. The advent of

smartphones and tablets is revolutionizing enterprise mobility. Judicious use of interfaces from the video

games industry brings playful applications, which allows more friendly use by customers. We talk about

"gamification"phenomenon,whichisabouttocommerciallyexplodeintheshortterm.

ConferencesonBigDatagrewquiteacrowdandallowedvisitorstodiscoveranemergingsectorthatshould

weighheavilyinthedevelopmentofenterprises.Inonly10years,theamountofdataincreasedexponentially.

Datastorageisacostlyproblemforbusinesses,butthesedataarerelativelyuntappedbycompanies.Theidea

of big data is to create added value from very diverse data. People now talk about flows, exchanges,

collaborationsratherthanstorage.Nothingissortedbuteverythingcanbefound.BigData(from10TBofdata)

isrevolutionizingtheinfrastructureininformationtechnology.EnvironmentssuchasHadoopprovideflexibility

in resources and adapt to the workload by adding inexpensive servers in parallel. Big Data has generated a

turnoverof$17billionin2011anditisestimatedthatthisfigurewilldoubleby2016.Thegreatdebatewith

bigdataistofindabalancebetweendatatransparencyandprivacyofcitizens.

Bigdataisrapidlyemergingasamarketforce,notjustasinglemarketuntoitself.BigDataITServicesSpending

will

attain

a

10.20%

CAGR

from

2011

to

2016.

By

2020,

big

data

functionality

will

be

part

of

the

baseline

of

enterprisesoftware,withenterprisevendorsenhancingthevalueoftheirapplicationswithit.


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PRESENTATIONOFVIEDOCSARL

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