rapport veille salon-mobileit&bigdata
TRANSCRIPT
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EXIBITIONWATCHREPORT
MobileIT&BigData
23rd
25th
ofOctober,2012Paris,PortedeVersailles
Applicationsformobile,BusinessIntelligence
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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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
2.4.3 Talend
Address:
TalendSA,9ruePags,
92150
SuresnesFranceWebsite:http://fr.talend.com/
Contact:
M.CdricCARBONE
Tel:+33
146
25
06
00
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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VIEDOCForanyfurtherinformation:[email protected]
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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VIEDOCForanyfurtherinformation:[email protected]
ABOUTVEILLESALON
Officially launched inearly2010byVIEDOCConsulting,abusiness&competitive&technological intelligence
company, VeilleSalon.com is the first professional service for watching and reporting on trade show
innovationsforcompaniesandisbasedononeofthelargestglobaldirectoryoftradeshows,symposiumsandotherinternationalevents.
This new professional service is designed both for visitors / companies, for exhibitors and trade show
organizers.
Through a bilingual directory, VEILLE SALON has already referenced more than 7,500 exhibitions and
internationaleventssortedandsearchableaccordingtobusinessareas:
for industrial sector : Aerospace, Agriculture, Agribusiness, Automotive, Materials, Construction,Consumer goods, Cosmetics, Electronics, Defense, Energy, Optics, Pharmaceuticals,
Telecommunications...
fortertiarysector:Banking/Insurance,Hospitality,RealEstate,Media/advertising,HumanServices,Tourism
...
for business area : Chemistry, Design / Architecture, Distribution, Packaging, Education / Training,Health&Environment,Computing,Innovation,Maintenance,Mechanical,Quality,HumanResources.
Besidesthepowerfulfeaturesofmulticriteriasearches(dates,places,keywords,sectors,organizers,exhibitors
...), VeilleSalon.com also offers visitors a customized and interactive calendar of forthcoming exhibitions, a
monthlynewsletter,aforumandmanyotherservices.
For potential exhibitorsand events organizers, VeilleSalon.com is a real communication tool: registration of
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freeorchargedconferenceproceedings,realtimeinformationforthevisitor...VeilleSalon.com isalsoaforum
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company.
Whyofferaprofessionalservicededicatedtotradeshowinnovationwatching?
Watchingtradeshowinnovationsisanidealwaytoidentifyandanalyzecompetitors,suppliers,newproducts,
equipment, and services, to detect technology transfers and innovations, to achieve business development
withpotentialnewcustomersandtoenhancemarketandtrendsknowledge.
Therefore the team VEILLESALON, through experienced consultants and seasoned business intelligence
engineersfromVIEDOCConsulting,offersarangeofservicesin:reportingontradeshowinnovations,inFrance
andabroad,supporting individualsonsiteevents,conductingondemand investigationsand interviews,staff
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Sowhetheryouareacompanywishingtomaximizeyourtradeshowinnovationwatch,afutureexhibitororan
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Toaccessourwebsite:http://www.veillesalon.com .
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VIEDOCForanyfurtherinformation:[email protected]
PRESENTATIONOFVIEDOCSARL
VIEDOC CONSULTINGs core business is information. VIEDOC is your companys partner from strategy to
operation.
VIEDOC aims to assist its customers in the first stages of their activities (Business intelligence, knowledge
management,competitive analysis, technological watch,market research,patent monitoring, benchmarking,
technologytransfers,stateoftheart...)throughinformationcollectandanalysisrelevanttoyourbusiness.
BusinessIntelligencedoesnotrequiremandatorylifelongskillswithinthecompanybutimposetogettheright
informationattherighttime.VIEDOChasworkedforcustomersbothonextendedandshortperiodsoftimeto
assistcompaniesindecisionmaking.
VIEDOC advises companies from all industries (automotive, aerospace and defense, food, cosmetics, health,
materials,optics,packaging,telecommunications...).
VIEDOCcan
assist
companies
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ambitious
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investing
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Fromthesmallinnovativecompanylookingforwardtohavingstrategicadviceintightmilestones,uptomajorindustrialgroupsanxioustokeeptheirleadershipposition.
Methodology:
Wehaveapragmaticapproachbuiltonarigorousmethodologyshowingthe issuesofcollecting,processing,
analyzinganddispatchingofinformationwithhighaddedvalueinformation.
Through its multisector experience, VIEDOC provides its clients with services tailored to their needs by
listeningtotheirconcernsandbeingavailabletomeettheirrequirementsandmethods.
Tosuccessfullyhelpitscustomersatdifferentstagesofthelifeoftheircompany(fromcreationtorecovery),of
theirproducts
(from
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sale)
or
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(from
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to
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project),
VIEDOC
operates both on process and on product innovation. VIEDOC deals both with technical and economical
information.
Youcanbenefitfromourexperience,ofspecialists incollectingandanalyzingvalueadded information,from
ourmethodologiesandanalyticalcapacitytoprovidequalifiedinformationandhighqualityvalidation.
As experts in technology transfer identification, we have consistently grown our multisectoral vision by
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This experience allows us today to make available to our customers, a meaningful analysis which does not
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