{"id":8470,"date":"2015-11-02T13:49:03","date_gmt":"2015-11-02T12:49:03","guid":{"rendered":"http:\/\/www.solucominsight.fr\/?p=8470"},"modified":"2015-11-04T17:24:37","modified_gmt":"2015-11-04T16:24:37","slug":"comprendre-la-jungle-technologique","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2015\/11\/comprendre-la-jungle-technologique\/","title":{"rendered":"Big data : comprendre la jungle technologique"},"content":{"rendered":"

Le Big data<\/a> est n\u00e9 d\u2019une rupture technologique\u00a0: la distribution des traitements et du stockage. L\u00e0 o\u00f9, en 2012, ce concept \u00e9tait novateur, il devient une offre \u00e0 part enti\u00e8re de la plupart des \u00e9diteurs du march\u00e9, cr\u00e9ant ainsi une jungle technologique dans laquelle il faut savoir se rep\u00e9rer. On compte aujourd\u2019hui plus d\u2019une centaine de solutions packag\u00e9es sous diff\u00e9rentes formes.<\/em><\/p>\n

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NAVIGUER DANS LA DIVERSIT\u00c9 DE SOLUTIONS<\/h1>\n

Quelles solutions r\u00e9pondront aux besoins pr\u00e9sents et futurs\u00a0?<\/p>\n

Trouver la solution ou la combinaison de solutions techniques est une phase critique d\u2019un projet Big data. Or quel que soit le secteur, il n\u2019y a pas de solution Big data universelle.
\nLe porte-\u00e9tendard technologique du Big data est Hadoop – un \u00e9cosyst\u00e8me open source<\/em> de distribution du stockage et des traitements sous l\u2019\u00e9gide de la fondation Apache. L\u2019ensemble des grands \u00e9diteurs et constructeurs ont investi dans cette technologie. Toutefois, d\u2019autres types de solutions sont toutes aussi l\u00e9gitimes suivant les usages. Ainsi, dans le cas o\u00f9 la rapidit\u00e9 d\u2019ex\u00e9cution est recherch\u00e9e, les technologies InMemory<\/a> sont \u00e0 privil\u00e9gier en raison de leur capacit\u00e9 \u00e0 stocker les donn\u00e9es en m\u00e9moire et non sur disque. Le NoSQL (Not Only SQL<\/a>) a quant \u00e0 lui un avantage dans le monde \u00ab\u00a0pseudo-transactionnel\u00a0\u00bb o\u00f9 il sera un acc\u00e9l\u00e9rateur dans le stockage et la manipulation de donn\u00e9es hors d\u2019une structure relationnelle. La vari\u00e9t\u00e9 des solutions implique une connaissance de l\u2019\u00e9cosyst\u00e8me technologique et ce d\u00e8s la phase \u00ab\u00a0test and try<\/em>\u00a0\u00bb.<\/p>\n

En 2015, Hadoop poss\u00e8de plus d\u2019une vingtaine de modules formant un socle riche. Ces modules sont cr\u00e9\u00e9s au fur et \u00e0 mesure par une importante, et dynamique, communaut\u00e9 d\u2019acteurs et d\u00e9veloppeurs open source<\/em>. Ils apportent constamment de nouvelles fonctionnalit\u00e9s tout en renfor\u00e7ant l\u2019environnement existant.<\/p>\n

Une illustration simple\u00a0: historiquement Hadoop ne savait traiter que les donn\u00e9es massives par des traitements de type batch<\/em>. Face \u00e0 ce constat, la communaut\u00e9 a enrichi l\u2019\u00e9cosyst\u00e8me par la cr\u00e9ation de modules de type Storm<\/em> ou Spark<\/em> permettant de traiter des donn\u00e9es unitaires en temps r\u00e9el et d\u00e9mocratisant encore plus l\u2019usage de plateformes Hadoop.<\/p>\n

Cette diversit\u00e9 de fonctions peut conduire \u00e0 de mauvais choix et par cons\u00e9quent certaines initiatives \u00e0 l\u2019\u00e9chec. Pour tirer le maximum de valeur des donn\u00e9es, il faut choisir les modules les plus adapt\u00e9s aux traitements d\u00e9sir\u00e9s et aux donn\u00e9es manipul\u00e9es. L\u2019expertise et la connaissance fine sont le pr\u00e9requis \u00e0 la cr\u00e9ation de valeur en vue d\u2019une industrialisation.<\/p>\n

LE BIG DATA SOUS DIFF\u00c9RENTES FORMES<\/h1>\n

Une fois la ou les solutions techniques choisies, il convient de d\u00e9finir le mode de distribution (m\u00eame si certaines solutions \u00a0peuvent imposer le leur). Quatre formes sont alors possibles :<\/p>\n

1 – La distribution construite et g\u00e9r\u00e9e par la DSI<\/h3>\n

L\u2019entreprise construit une infrastructure Big data en agr\u00e9geant ses infrastructures conventionnelles via l\u2019int\u00e9gration d\u2019une distribution. Ce type de solution est principalement destin\u00e9 aux entreprises ayant un besoin d\u2019\u00e9volutivit\u00e9 et d\u2019appropriation fort.<\/p>\n

2 – L\u2019\u00ab appliance \u00bb ou solution cl\u00e9 en main<\/h3>\n

Les grands \u00e9diteurs comme Oracle, Microsoft, IBM et les constructeurs proposent une infrastructure sur \u00e9tag\u00e8re o\u00f9 mat\u00e9riel et logiciels sont int\u00e9gr\u00e9s, pr\u00e9configur\u00e9s et optimis\u00e9s. Ce mod\u00e8le a l\u2019avantage d\u2019une efficacit\u00e9 op\u00e9rationnelle imm\u00e9diate en contrepartie d\u2019un co\u00fbt d\u2019investissement qui peut s\u2019av\u00e9rer \u00e9lev\u00e9.<\/p>\n

3 – La \u00ab\u00a0plateforme as a Service\u00a0\u00bb ou BDaaS<\/em><\/h3>\n

Cette formule connue bas\u00e9e sur le Cloud provisionne un environnement op\u00e9rationnel et \u00e9lastique comprenant l\u2019ensemble des composants Big data pr\u00e9 configur\u00e9s. L\u2019int\u00e9r\u00eat de ces offres Cloud est le co\u00fbt (\u00e0 l\u2019usage) et la rapidit\u00e9 de mise en place, qui en font une cible parfaite pour des usages de type POC afin de tester la valeur du Big data pour l\u2019entreprise.\u00a0En production, le gain sera plus limit\u00e9 du fait des contraintes li\u00e9es \u00e0 l\u2019expatriation des donn\u00e9es \u00e0 l\u2019ext\u00e9rieur de l\u2019entreprise.<\/p>\n

4 – Le \u00ab\u00a0Software as a Service\u00a0\u00bb ou SaaS<\/em><\/h3>\n

Le SaaS<\/em>, une formule connue depuis l\u2019arriv\u00e9e du Cloud, permet la mise \u00e0 disposition de services m\u00e9tiers sur le web. Le Big data donne un acc\u00e9l\u00e9rateur \u00e0 ce mod\u00e8le en permettant la cr\u00e9ation de nouveaux services comme l\u2019analyse des r\u00e9seaux sociaux, l\u2019e-r\u00e9putation, le marketing cibl\u00e9\u2026 Ces services reposent sur des moteurs Big data n\u00e9anmoins invisibles par les utilisateurs finaux.<\/p>\n

Dans le secteur de l\u2019Assurance, les 4 formules sont l\u00e9gitimes et d\u00e9pendent r\u00e9ellement du souhait d\u2019investissement et d\u2019appropriation de la technologie. La complexit\u00e9 de ces solutions laisse \u00e0 penser que le march\u00e9 va tendre vers une externalisation des probl\u00e9matiques techniques favorisant les offres de type appliance ou Cloud.<\/p>\n

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Le Big data est n\u00e9 d\u2019une rupture technologique\u00a0: la distribution des traitements et du stockage. 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