{"id":3570,"date":"2013-03-26T10:35:19","date_gmt":"2013-03-26T09:35:19","guid":{"rendered":"http:\/\/www.solucominsight.fr\/?p=3570"},"modified":"2015-08-17T10:16:38","modified_gmt":"2015-08-17T09:16:38","slug":"big-data-comment-integrer-les-technologies-de-stockage","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2013\/03\/big-data-comment-integrer-les-technologies-de-stockage\/","title":{"rendered":"Big data : comment int\u00e9grer les technologies de stockage ?"},"content":{"rendered":"

Les solutions Big data orient\u00e9es stockage sont de plus en plus nombreuses et commencent \u00e0 \u00eatre d\u00e9ploy\u00e9es dans les entreprises. Tout le monde doit-il s\u2019y int\u00e9resser\u00a0? Comment doit-on les int\u00e9grer\u00a0?<\/em><\/p>\n

Quelles sont caract\u00e9ristiques du Big data<\/em>\u00a0?<\/h2>\n

Comme son nom l\u2019indique, le Big data<\/em> d\u00e9finit une cat\u00e9gorie de donn\u00e9es. Elle est souvent r\u00e9sum\u00e9e par les 3 \u00ab\u00a0V\u00a0\u00bb\u00a0:<\/p>\n

    \n
  • Volume\u00a0: <\/strong>une quantit\u00e9 de donn\u00e9es importante li\u00e9e \u00e0 la multiplication des donn\u00e9es du client. Les donn\u00e9es \u00e0 manipuler sont de l\u2019ordre de la dizaine de tera octets\u00a0;<\/li>\n
  • Vari\u00e9t\u00e9\u00a0:<\/strong> diff\u00e9rents types de donn\u00e9es \u00a0provenant de diverses sources (internes SI, externes\u00a0comme les r\u00e9seaux sociaux\u2026 Ces donn\u00e9es vont du plus structur\u00e9 (relationnel) au non structur\u00e9 (fichiers, vid\u00e9o\u2026)\u00a0;<\/li>\n
  • V\u00e9locit\u00e9<\/strong>\u00a0: une fr\u00e9quence rapide \u00e0 laquelle les donn\u00e9es doivent \u00eatre trait\u00e9es et partag\u00e9es, li\u00e9e \u00e0 la volont\u00e9 de tendre vers un SI \u00ab\u00a0temps r\u00e9el\u00a0\u00bb.<\/li>\n<\/ul>\n

    \u00a0Comment savoir si l\u2019on manipule des donn\u00e9es Big data<\/em>\u00a0?<\/h2>\n

    C\u2019est simple, c\u2019est souvent lorsque l\u2019une des couches du SI devient un facteur limitant lors du traitement de gros volume de donn\u00e9es, que l\u2019on comprend qu\u2019il s\u2019agit de Big data<\/em>.<\/p>\n

    La couche stockage est l\u2019un des premiers facteurs limitant et les probl\u00e9matiques associ\u00e9es sont les suivantes :<\/p>\n

      \n
    • Performance :<\/strong> l\u2019augmentation du volume \u00e0 traiter entra\u00eene une diminution des performances\u00a0;<\/li>\n
    • Lin\u00e9arit\u00e9\u00a0:<\/strong> l\u2019outillage n\u2019\u00e9tant pas adapt\u00e9 aux gros volumes de donn\u00e9es, le mod\u00e8le de scalabilit\u00e9 n\u2019est pas lin\u00e9aire, l\u2019ajout de stockage ne permettant pas d\u2019am\u00e9liorer les performances\u00a0;<\/li>\n
    • Dynamisme\u00a0: <\/strong>l\u2019allocation d\u2019espace est fixe avec une faible r\u00e9activit\u00e9 pour en ajouter ou en retirer.<\/li>\n<\/ul>\n

      Les solutions du march\u00e9 reposent sur des impl\u00e9mentations propri\u00e9taires.<\/p>\n

      Les solutions Big data<\/em> reposent sur un stockage bas\u00e9 sur le mod\u00e8le de fichier distribu\u00e9 <\/strong>: des n\u0153uds de stockage sont r\u00e9partis physiquement sur le r\u00e9seau mais vus par les applications comme un seul volume de stockage logique.
      \n<\/em><\/p>\n

      Ce mod\u00e8le r\u00e9pond justement aux enjeux suivants, auparavant probl\u00e9matiques\u00a0:<\/p>\n

        \n
      • Performance\u00a0<\/strong>: les donn\u00e9es sont r\u00e9parties sur plusieurs n\u0153uds de stockage (stripping HADOOP HDFS<\/em>) et ceux-ci se distribuent intelligemment la donn\u00e9e afin de diminuer le trafic r\u00e9seau (les donn\u00e9es semblables sur un m\u00eame n\u0153ud) et faciliter des traitements distribu\u00e9s (HADOOP map reduce<\/em>)<\/li>\n
      • Lin\u00e9arit\u00e9<\/strong>\u00a0: le mod\u00e8le distribu\u00e9 permet d\u2019ajouter des n\u0153uds de stockage sans limite et permet de retrouver une scalabilit\u00e9 lin\u00e9aire\u00a0;<\/li>\n
      • Dynamisme<\/strong>\u00a0: l\u2019ajout et la suppression de n\u0153ud de calcul peuvent se faire simplement et apportent une r\u00e9silience (via la r\u00e9plication automatique des donn\u00e9es). Si un n\u0153ud de stockage tombe, le service est assur\u00e9 sans arr\u00eat et sans perte de donn\u00e9es. On s\u2019approche d\u2019une perte de donn\u00e9es \u00ab\u00a0RPO\u00a0\u00bb et d\u2019un temps de reprise \u00ab\u00a0RTO\u00a0\u00bb nulle.<\/li>\n<\/ul>\n

        Les solutions <\/strong>du march\u00e9 (IBM, EMC, etc.) r\u00e9pondent aux 3 \u00ab\u00a0v\u00a0\u00bb du Big data<\/em><\/strong> mais chacune suit son propre mod\u00e8le d\u2019impl\u00e9mentation du stockage distribu\u00e9<\/strong>\u00a0: Cluster File System<\/em>, Parallel File System\u2026. Chaque solution n\u2019a donc pas les m\u00eames performances ou, capacit\u00e9 d\u2019\u00e9volutivit\u00e9 suivant le besoin.<\/p>\n

        \u00a0La qualification des donn\u00e9es est la cl\u00e9 du stockage<\/h2>\n

        C\u2019est pour cela que lors de la mise en \u0153uvre de ces types de solutions, une \u00e9tude sur le stockage est n\u00e9cessaire. <\/strong>\u00a0Quelles sont mes donn\u00e9es\u00a0? Quelle est la volum\u00e9trie (max, \u00e9cart-type, moyenne) ? Quelle est leur croissance\u00a0? Quels types de traitements sont effectu\u00e9s\u00a0? Doivent-elles \u00eatre centralis\u00e9es\u00a0? Quel est le ratio de lecture \/ \u00e9criture\u00a0? \u2026<\/p>\n

        Toutes ces r\u00e9ponses permettront de cat\u00e9goriser les donn\u00e9es<\/strong>, un entrant primordial \u00e0 la qualification<\/strong> de la solution cible et \u00e0 son optimisation. Ainsi l\u2019optimisation des\u00a0 services de stockage Big data<\/em> permettent \u00e0 l\u2019entreprise de ma\u00eetriser la variabilit\u00e9 et les performances. L\u2019indexation devient plus facile, la taille des caches est optimis\u00e9e et le stripping<\/em> (fait de couper le fichier en plusieurs morceaux) est facilit\u00e9 afin de garantir un acc\u00e8s plus rapide \u00e0 la donn\u00e9e.<\/p>\n

        La mise en place d\u2019une technologie stockage distribu\u00e9 est id\u00e9ale dans les configurations de type grille de calcul<\/strong>\u00a0: les n\u0153uds de calcul \u00e9changent un grand nombre de donn\u00e9es entre eux. \u00c0 \u00a0la place d\u2019une configuration en \u00e9toile o\u00f9 tous les n\u0153uds s\u2019\u00e9changent directement des fichiers, chaque n\u0153ud utilise un m\u00eame volume logique h\u00e9berg\u00e9 sur une infrastructure Big data. Les gains sont doubles\u00a0: directement sur les temps de lecture \/ \u00e9criture des donn\u00e9es partag\u00e9es et la charge r\u00e9seau (bien que plus concentr\u00e9e sur un segment) et indirectement sur les temps de calculs de la grille. Les n\u0153uds de la grille de calcul \u00e9tant d\u00e9charg\u00e9s de leur fonction de distribution de fichiers vers les autres n\u0153uds, les ressources sont exploit\u00e9es au maximum par le calcul applicatif. L\u2019applicatif est donc plus performant pour le business<\/em>.<\/p>\n

        Les solutions de stockage Big data <\/em>commencent \u00e0 \u00eatre adopt\u00e9es par les entreprises, qui les utilisent sur leurs p\u00e9rim\u00e8tres strat\u00e9giques <\/strong>pour en tirer des gains de performance<\/strong> l\u00e0 o\u00f9 les solutions standards \u00e9taient limitantes. L\u2019int\u00e9gration de ces solutions reste l\u2019\u00e9tape cl\u00e9 <\/strong>: l\u2019\u00e9tude des donn\u00e9es est n\u00e9cessaire pour choisir la bonne impl\u00e9mentation Big data<\/em>, faciliter sa configuration et ainsi profiter pleinement des \u00a0b\u00e9n\u00e9fices annonc\u00e9s.<\/p>\n

        \n
        \n<\/div>\n","protected":false},"excerpt":{"rendered":"

        Les solutions Big data orient\u00e9es stockage sont de plus en plus nombreuses et commencent \u00e0 \u00eatre d\u00e9ploy\u00e9es dans les entreprises. Tout le monde doit-il s\u2019y int\u00e9resser\u00a0? Comment doit-on les int\u00e9grer\u00a0? Quelles sont caract\u00e9ristiques du Big data\u00a0? Comme son nom l\u2019indique,…<\/p>\n","protected":false},"author":157,"featured_media":2895,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"page-templates\/tmpl-one.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[35],"tags":[189,59,321,1054,1055,68,1058,224,1057,316,1056],"coauthors":[1059,1060],"class_list":["post-3570","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-strategie-projets-it","tag-big-data","tag-donnees","tag-hadoop","tag-hdfs","tag-map-reduce","tag-performance","tag-scalabilite","tag-stockage","tag-variete","tag-velocite","tag-volume"],"acf":[],"yoast_head":"\nBig data : comment int\u00e9grer les technologies de stockage ?<\/title>\n<meta name=\"description\" content=\"Les solutions Big data orient\u00e9es stockage sont de plus en plus nombreuses et commencent \u00e0 \u00eatre d\u00e9ploy\u00e9es dans les entreprises. 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