{"id":3978,"date":"2013-08-02T15:43:43","date_gmt":"2013-08-02T14:43:43","guid":{"rendered":"http:\/\/www.solucominsight.fr\/?p=3978"},"modified":"2020-01-02T10:57:39","modified_gmt":"2020-01-02T09:57:39","slug":"auw-origines-du-big-data","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2013\/08\/auw-origines-du-big-data\/","title":{"rendered":"Aux origines du Big data…"},"content":{"rendered":"

Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es <\/a>: l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les technologies pour les traiter. \u00a0Pour comprendre ces concepts et imaginer leurs d\u00e9veloppements futurs, il est indispensable d\u2019en conna\u00eetre l\u2019origine et l\u2019\u00e9volution. Revenons bri\u00e8vement sur la petite histoire – \u00e0 forts rebondissements – du Big data.<\/p>\n

De l\u2019explosion des donn\u00e9es au Big data<\/h2>\n

L\u2019expression de \u00ab\u00a0petite histoire du Big data\u00a0\u00bb, nous l\u2019empruntons (presque) \u00e0 Gil Press. Il livre en effet dans un long article document\u00e9 sur Forbes.com, dat\u00e9 du 5 mai 2013,\u00a0\u00ab\u00a0une tr\u00e8s courte histoire du big data<\/a>\u00a0\u00bb. En une trentaine de dates de 1944 \u00e0 2012, il retrace la prise de conscience pr\u00e9coce de l\u2019explosion des donn\u00e9es (dans les rayons des biblioth\u00e8ques universitaires dans un premier temps, puis via les NTIC), avec comme pr\u00e9occupation centrale d\u2019en quantifier la croissance tout en apportant\u00a0 quelques consid\u00e9rations critiques autour du d\u00e9luge informationnel.<\/p>\n

L\u2019explosion des donn\u00e9es est en effet \u00a0d\u2019abord per\u00e7ue comme une menace sur la vie priv\u00e9e (\u00ab\u00a0Assault on privacy <\/em>– 1971-\u00a0 Arthur Miller).\u00a0 C\u00f4t\u00e9 technique aussi\u00a0: l\u2019espace de stockage grandit, mais les donn\u00e9es s\u2019\u00e9tendent syst\u00e9matiquement jusqu\u2019\u00e0 le combler (Loi de parkinson, 1980). Dans les ann\u00e9es 70, la qualit\u00e9 des donn\u00e9es est enfin mise en cause\u00a0: tout est stock\u00e9, il n\u2019est plus utile de faire le tri.<\/p>\n

L\u2019expression \u00ab\u00a0Big data\u00a0\u00bb fait finalement son apparition en octobre 1997 dans la biblioth\u00e8que num\u00e9rique de l\u2019ACM1<\/sup>, au sein d\u2019articles scientifiques qui pointent du doigt les d\u00e9fis technologiques \u00e0 visualiser les \u00ab\u00a0grands ensembles de donn\u00e9es\u00a0\u00bb. Le Big data est n\u00e9, et avec lui ses nombreux d\u00e9fis.<\/p>\n

Et si ces donn\u00e9es avaient de la valeur\u00a0?<\/h2>\n

Dans les ann\u00e9es 2000, alors que l\u2019exabytes2<\/sup> entrent en jeu dans la quantification des donn\u00e9es produites annuellement, la valeur du Big data est mise en avant, d\u2019abord pour les b\u00e9n\u00e9fices que peuvent en tirer la recherche dans les secteurs de la physique, de la biologie ou des sciences sociales.<\/p>\n

Ce que n\u2019illustre pas l\u2019article du Forbes.com, c\u2019est la mont\u00e9e en puissance des Google, Facebook, Linkedin, Amazon et Twitter dans les ann\u00e9es 2000 et plus particuli\u00e8rement \u00e0 partir de 2005. Celle-ci r\u00e9v\u00e8le tout le potentiel des donn\u00e9es publiques disponibles sur internet. Les succ\u00e8s \u00e9conomiques des grands du web commencent alors \u00e0 nourrir deux id\u00e9es\u00a0principales :<\/p>\n

1- Les donn\u00e9es brutes accumul\u00e9es ont une valeur intrins\u00e8que de par les fameuses 3 composantes en V ( pour volume essentiellement mais aussi vari\u00e9t\u00e9 et vitesse de leur production donc leur fra\u00eecheur). Cette valeur est mon\u00e9tisable et Facebook en est l\u2019illustration par excellence\u00a0!<\/p>\n

2- Ces donn\u00e9es brutes ont une autre valeur li\u00e9e aux analyses et corr\u00e9lations auxquelles elles s\u2019offrent et l\u2019information qui en d\u00e9coule participe \u00e0 la cha\u00eene de valeur de l\u2019entreprise (ex.\u00a0: l\u2019algorithme de recommandation d\u2019Amazon).<\/p>\n

Quelques principes technologiques qui expliquent la mont\u00e9e en puissance du Big data<\/h2>\n

Derri\u00e8re ces succ\u00e8s, il y a \u00e9videmment des technologies qui font la diff\u00e9rence et que les pr\u00e9curseurs du Big data ont contribu\u00e9 \u00e0 d\u00e9velopper et \u00e0 faire conna\u00eetre. \u00a0Les architectures Big data se fondent d\u2019ailleurs sur 3 principes technologiques cl\u00e9s :<\/p>\n

    \n
  • L\u2019acc\u00e9l\u00e9ration mat\u00e9rielle.<\/strong> Depuis toujours, les composants des syst\u00e8mes (m\u00e9moire, processeur, bus) sont optimis\u00e9s pour assurer toujours plus efficacement leur fonction.<\/li>\n
  • Le parall\u00e9lisme.<\/strong>\u00a0 C\u2019est dans les ann\u00e9es 2000 que ce paradigme devient dominant. Qu\u2019il s\u2019agisse de l\u2019architecture interne d\u2019un ordinateur (multi-cores, multi-processeurs, MPP*, SMP**) ou de l\u2019architecture d\u2019un r\u00e9seau de machines (grid computing<\/em>, cluster de machines, cluster hadoop), l\u2019id\u00e9e est de d\u00e9composer les probl\u00e8mes complexes en plusieurs calculs trait\u00e9s de mani\u00e8re simultan\u00e9e. Ces architectures sont d\u2019autant plus efficaces qu\u2019aucun composant n\u2019est partag\u00e9 entre les unit\u00e9s de traitement parall\u00e8les (\u00ab\u00a0shared nothing<\/em>\u00a0\u00bb)\u00a0et qu\u2019il n\u2019y a donc pas de point de contention.<\/li>\n
  • Le NoSQL<\/strong>, le mouvement d\u00e9colle fin des ann\u00e9es 2000 et promeut des alternatives aux bases de donn\u00e9es relationnelles en en abandonnant certaines contraintes (notamment la consistance du th\u00e9or\u00e8me CAP3<\/sup>.)<\/li>\n<\/ul>\n

    Vous l\u2019aurez compris, le Big data a \u00e9merg\u00e9 en 2010, mais il est n\u00e9 il y a quinze ans et il porte en lui un demi-si\u00e8cle de r\u00e9flexions, de concepts et de d\u00e9fis. Participerez-vous \u00e0 son histoire\u00a0?<\/p>\n

     <\/p>\n

    \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..
    \n*MPP :<\/strong> Massively Parallel Processing<\/em>, \u00e0 la diff\u00e9rence du SMP, le MPP appliqu\u00e9 \u00e0 l’architecture d’un ordinateur consiste en la mise en \u0153uvre parall\u00e8le d’un grand nombre de processeurs disposant chacun de leur m\u00e9moire.<\/p>\n

    **SMP : <\/strong>Symetric MultiProcessing<\/em>,\u00a0 architecture parall\u00e8le constitu\u00e9e de plusieurs processeurs identiques partageant une m\u00eame m\u00e9moire au sein d’un m\u00eame ordinateur.<\/p>\n


    <\/p>\n
    \n
    \n

    1<\/sup> Association For Computing Machinery, association am\u00e9ricaine \u00e0 but non lucratif fond\u00e9e en 1947 et vou\u00e9e \u00e0 l\u2019informatique. Sa biblioth\u00e8que num\u00e9rique est particuli\u00e8rement riche quand il s\u2019agit de retracer l\u2019histoire de la discipline.<\/p>\n<\/div>\n

    \n

    2 \u00a0 <\/sup>1000 petabytes.<\/p>\n<\/div>\n

    \n

    3 <\/sup>Le th\u00e9or\u00e8me CAP explique que pour un syst\u00e8me r\u00e9parti (tel qu\u2019une base de donn\u00e9es r\u00e9partie sur plusieurs ordinateurs), il n\u2019est pas possible d\u2019assurer simultan\u00e9ment : coh\u00e9rence, disponibilit\u00e9 (Availability<\/em>), r\u00e9sistance au partitionnement (Partition Tolerance<\/em>).<\/p>\n<\/div>\n<\/div>\n

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

    Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es : l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les…<\/p>\n","protected":false},"author":40,"featured_media":3841,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"page-templates\/tmpl-one.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[35],"tags":[189,187,321,1242,1245,1244,1243,730],"coauthors":[883],"class_list":["post-3978","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-strategie-projets-it","tag-big-data","tag-grid-computing","tag-hadoop","tag-histoire","tag-nosql","tag-parallelisme","tag-shared-nothing","tag-valeur"],"acf":[],"yoast_head":"\nAux origines du Big data...<\/title>\n<meta name=\"description\" content=\"Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es : l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les technologies pour les traiter. Pour comprendre ces concepts et imaginer leurs d\u00e9veloppements futurs, il est indispensable d\u2019en conna\u00eetre l\u2019origine et l\u2019\u00e9volution. Revenons bri\u00e8vement sur la petite histoire - \u00e0 forts rebondissements - du Big data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Aux origines du Big data...\" \/>\n<meta property=\"og:description\" content=\"Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es : l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les technologies pour les traiter. 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Revenons bri\u00e8vement sur la petite histoire - \u00e0 forts rebondissements - du Big data.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\" \/>\n<meta property=\"og:site_name\" content=\"RiskInsight\" \/>\n<meta property=\"article:published_time\" content=\"2013-08-02T14:43:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-01-02T09:57:39+00:00\" \/>\n<meta name=\"author\" content=\"Lise Gasnier\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Lise Gasnier\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\"},\"author\":{\"name\":\"Lise Gasnier\",\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/en\/#\/schema\/person\/e11e1ee41e3bea855ef571bae7977441\"},\"headline\":\"Aux origines du Big data…\",\"datePublished\":\"2013-08-02T14:43:43+00:00\",\"dateModified\":\"2020-01-02T09:57:39+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\"},\"wordCount\":948,\"publisher\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/en\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage\"},\"thumbnailUrl\":\"\",\"keywords\":[\"Big Data\",\"grid computing\",\"Hadoop\",\"histoire\",\"NoSQL\",\"parall\u00e8lisme\",\"shared nothing\",\"valeur\"],\"articleSection\":[\"M\u00e9tiers - Strat\u00e9gie & projets IT\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\",\"url\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/\",\"name\":\"Aux origines du Big data...\",\"isPartOf\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/en\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage\"},\"thumbnailUrl\":\"\",\"datePublished\":\"2013-08-02T14:43:43+00:00\",\"dateModified\":\"2020-01-02T09:57:39+00:00\",\"description\":\"Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es : l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les technologies pour les traiter. 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Revenons bri\u00e8vement sur la petite histoire - \u00e0 forts rebondissements - du Big data.","og_url":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/","og_site_name":"RiskInsight","article_published_time":"2013-08-02T14:43:43+00:00","article_modified_time":"2020-01-02T09:57:39+00:00","author":"Lise Gasnier","twitter_misc":{"Written by":"Lise Gasnier","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#article","isPartOf":{"@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/"},"author":{"name":"Lise Gasnier","@id":"https:\/\/www.riskinsight-wavestone.com\/en\/#\/schema\/person\/e11e1ee41e3bea855ef571bae7977441"},"headline":"Aux origines du Big data…","datePublished":"2013-08-02T14:43:43+00:00","dateModified":"2020-01-02T09:57:39+00:00","mainEntityOfPage":{"@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/"},"wordCount":948,"publisher":{"@id":"https:\/\/www.riskinsight-wavestone.com\/en\/#organization"},"image":{"@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage"},"thumbnailUrl":"","keywords":["Big Data","grid computing","Hadoop","histoire","NoSQL","parall\u00e8lisme","shared nothing","valeur"],"articleSection":["M\u00e9tiers - Strat\u00e9gie & projets IT"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/","url":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/","name":"Aux origines du Big data...","isPartOf":{"@id":"https:\/\/www.riskinsight-wavestone.com\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage"},"image":{"@id":"https:\/\/www.riskinsight-wavestone.com\/2013\/08\/auw-origines-du-big-data\/#primaryimage"},"thumbnailUrl":"","datePublished":"2013-08-02T14:43:43+00:00","dateModified":"2020-01-02T09:57:39+00:00","description":"Comme nous l\u2019avions vu dans un pr\u00e9c\u00e9dent article, derri\u00e8re le Big data se cache un bon nombre d\u2019id\u00e9es : l\u2019explosion des donn\u00e9es (volume, vari\u00e9t\u00e9 et v\u00e9locit\u00e9), la potentialit\u00e9 de valeur que l\u2019on peut en tirer, sans oublier bien s\u00fbr les technologies pour les traiter. Pour comprendre ces concepts et imaginer leurs d\u00e9veloppements futurs, il est indispensable d\u2019en conna\u00eetre l\u2019origine et l\u2019\u00e9volution. 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