{"id":633,"date":"2021-06-28T11:51:22","date_gmt":"2021-06-28T09:51:22","guid":{"rendered":"https:\/\/datapy.fr\/?p=633"},"modified":"2024-03-27T16:23:00","modified_gmt":"2024-03-27T15:23:00","slug":"athena-solution-serverless-damazon-mise-en-perspective-de-buzz-query-moteur-de-requetes-de-dashboarding-serverless-bigdata","status":"publish","type":"post","link":"https:\/\/datapy.fr\/index.php\/2021\/06\/28\/athena-solution-serverless-damazon-mise-en-perspective-de-buzz-query-moteur-de-requetes-de-dashboarding-serverless-bigdata\/","title":{"rendered":"Athena, solution serverless d\u2019Amazon, mise en perspective de \u201cBuzz Query\u201d, moteur de requ\u00eates de dashboarding serverless bigdata"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"633\" class=\"elementor elementor-633\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-52dad423 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"52dad423\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2f5719de\" data-id=\"2f5719de\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-310a52a elementor-widget elementor-widget-image\" data-id=\"310a52a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 25-04-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"552\" src=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-1024x552.jpeg\" class=\"attachment-large size-large wp-image-3186\" alt=\"\" srcset=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-1024x552.jpeg 1024w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-300x162.jpeg 300w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-768x414.jpeg 768w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-1536x829.jpeg 1536w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-2048x1105.jpeg 2048w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210628-DataPy_Amazon_Athena-e1711552942685.jpeg 1050w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-56a8f9d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"56a8f9d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d02f883\" data-id=\"d02f883\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8082509 elementor-widget elementor-widget-heading\" data-id=\"8082509\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 25-04-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">\nAthena, solution serverless d\u2019Amazon, mise en perspective de \u201cBuzz Query\u201d, moteur de requ\u00eates de dashboarding serverless bigdata<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-78a2095 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"78a2095\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d2e44c2\" data-id=\"d2e44c2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3498608 elementor-widget elementor-widget-text-editor\" data-id=\"3498608\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 25-04-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Nous avions explor\u00e9 dans l\u2019article pr\u00e9c\u00e9dent les forces et faiblesses de Spark et Elasticsearch. Nous allons \u00e0 pr\u00e9sent creuser en quoi la solution Athena d\u2019Amazon est une brique pertinente, notamment quand elle est int\u00e9gr\u00e9e dans une solution comme celle de BuzzQuery de CloudFuse.io pour une solution de moteur de requ\u00eate de dashboarding bigdata.<\/p><p>Amazon a construit Athena dans le but de faire des requ\u00eates SQL en serverless. Le stockage s\u2019appuie sur S3. Athena s\u2019appuie sur une flotte de serveurs Presto qui sont \u00e9videmment stock\u00e9s chez Amazon. Amazon s\u2019occupe ensuite de g\u00e9n\u00e9rer la requ\u00eate r\u00e9sultat. Un avantage d\u2019Athena est qu\u2019il est gratuit si on ne l\u2019utilise pas (principe commun en serverless). Athena est utilisable quelle que soit la taille de la base de donn\u00e9es, qu\u2019elle soit grande ou petite.\u00a0<\/p><p>Au-del\u00e0 d\u2019atouts certains, Athena ne fournit pas de niveau de services garantis (SLA), ie d\u2019avoir un serveur Presto pour n\u2019importe quelle requ\u00eate. Cela peut devenir prohibitif dans le cas de certains applicatifs.\u00a0<\/p><p>Un autre inconv\u00e9nient est l\u2019absence de cache. Si on fait deux fois des requ\u00eates proches, les performances de la seconde requ\u00eate ne seront pas optimis\u00e9es.<\/p><p>Le prix et la puissance de calcul d\u00e9pendent de la quantit\u00e9 de donn\u00e9es utilis\u00e9e. 1 To = 5 $ (environ). Il peut y avoir une inadaptation entre les capacit\u00e9s de calculs et les besoins en calculs, ce qui peut bloquer certaines requ\u00eates.\u00a0<\/p><p>C\u2019est le revers de la m\u00e9daille que d\u2019avoir beaucoup simplifi\u00e9.\u00a0<\/p><p>La solution BigQuery de Google est tr\u00e8s similaire \u00e0 Athena. Ils n\u2019utilisent pas Presto. Il semble qu\u2019il y ait un peu de flexibilit\u00e9, par exemple avec une provision de calcul customizable\u2026 Mais on perd alors le serverless.<\/p><p>On voit donc poindre le besoin d\u2019une solution en pay-as-you-go, donc serverless qui aurait un SLA en termes de temps de latence.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c7d753c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c7d753c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0a98e2c\" data-id=\"0a98e2c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-52b6ce2 elementor-widget elementor-widget-text-editor\" data-id=\"52b6ce2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>La solution d\u00e9velopp\u00e9e par CloudFuse.io (R\u00e9mi DETTAI) s\u2019appelle \u201cBuzz Query\u201d. Son but est donc de proposer une solution \u00e0 faible latence \u00e0 bon march\u00e9, permettant une interactivit\u00e9 suffisante pour les utilisateurs finaux.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-03020ac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"03020ac\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-84e46ad\" data-id=\"84e46ad\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b4978cd elementor-widget elementor-widget-heading\" data-id=\"b4978cd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Mais comment construire une telle solution?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d4c3d2 elementor-widget elementor-widget-text-editor\" data-id=\"5d4c3d2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Buzz Query va tirer profit d\u2019un composant clef d\u2019Amazon, les Lambdas, et les combiner avec des solutions issues du monde de l\u2019embarqu\u00e9 pour leur d\u00e9clenchement, en moins de 50 ms.<\/p><p>AWS Lambda est un service de calcul sans serveur qui vous permet d\u2019ex\u00e9cuter du code sans provisionner ou g\u00e9rer des serveurs, cr\u00e9er une logique de dimensionnement de cluster prenant en charge la charge de travail, maintenir les int\u00e9grations d\u2019\u00e9v\u00e9nements ou g\u00e9rer les environnements d\u2019ex\u00e9cution. Les Lambdas ont justement une propri\u00e9t\u00e9 int\u00e9ressante, leur faible dur\u00e9e d\u2019activation.\u00a0Ainsi, pour compenser le fait que la donn\u00e9e est stock\u00e9e sur un stockage froid et peu co\u00fbteux, en l\u2019occurrence Amazon S3, le moteur est capable de parall\u00e9liser la requ\u00eate massivement sur un tr\u00e8s grand nombre de machines (plusieurs milliers).<\/p><p>R\u00e9mi utilise \u00e9galement un maximum de composants existants, comme Apache Arrow, un projet Open Source tr\u00e8s versatile et dynamique qui est en train de cr\u00e9er un standard du stockage des donn\u00e9es en m\u00e9moires.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1e96641 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1e96641\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1988d15\" data-id=\"1988d15\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-36750ce elementor-widget elementor-widget-heading\" data-id=\"36750ce\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">A qui pourrait b\u00e9n\u00e9ficier Buzz Query?\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f059eb0 elementor-widget elementor-widget-text-editor\" data-id=\"f059eb0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>R\u00e9mi Dettai a justement eu le besoin d\u2019une telle solution alors qu\u2019il travaillait pour une startup dans le domaine de la pub. De gros volumes de donn\u00e9es. Un besoin d\u2019interactivit\u00e9 c\u00f4t\u00e9 utilisateur. Des besoins fonctionnels pas encore clarifi\u00e9s, donc une barri\u00e8re \u00e0 l\u2019agr\u00e9gation pr\u00e9liminaire. Des moyens financiers limit\u00e9s dans ce contexte. C\u2019est donc de cette exp\u00e9rience qu\u2019est venue le besoin d\u2019une solution comme Buzz Query. Par extension, nous pouvons donc imaginer que d\u2019autres adopteurs potentiels: des leaders de projet d\u2019analytics pas encore mature en termes d\u2019usages, donc peu \u00e0 m\u00eame que quantifier les ressources \u00e0 provisionner en terme de capacit\u00e9 de calcul, n\u00e9cessitant de gros volumes de donn\u00e9es, et d\u00e9sireux de fournir un dashboard interactif \u00e0 l\u2019utilisateur. S\u2019ils devaient provisionner un cluster, \u00e7a leur co\u00fbterait cher. Athena serait un peu lent. L\u2019int\u00e9gration des Lambda d\u2019Amazon que propose R\u00e9mi pourrait \u00eatre la solution.<\/p><p>Amis geeks du BigData, venez donc contribuer<\/p><p>Paris ne s\u2019est pas fait en un jour. La solution de R\u00e9mi est par conviction en open source. R\u00e9mi a besoin de contributeurs pour consolider, p\u00e9renniser et faire \u00e9voluer sa solution. N\u2019h\u00e9sitez pas \u00e0 le contacter via LinkedIn, sur GitHub (<a href=\"https:\/\/github.com\/cloudfuse-io\/buzz-rust\">https:\/\/github.com\/cloudfuse-io\/buzz-rust<\/a>), ou via son site web (<a href=\"https:\/\/www.cloudfuse.io\/\">https:\/\/www.cloudfuse.io\/<\/a>). Il en sera ravi.<\/p><p>Et vous, quelle solution de dashboarding bigdata avez-vous d\u00e9ploy\u00e9e?<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dfcc89f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dfcc89f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6719a7b\" data-id=\"6719a7b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ffd4327 elementor-position-left elementor-view-default elementor-mobile-position-top elementor-vertical-align-top elementor-widget elementor-widget-icon-box\" data-id=\"ffd4327\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<link rel=\"stylesheet\" href=\"https:\/\/datapy.fr\/wp-content\/plugins\/elementor\/assets\/css\/widget-icon-box.min.css\">\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon elementor-animation-\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-angle-right\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tVous devriez \u00e9galement aimer\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bd67834 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bd67834\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-94ba7ed\" data-id=\"94ba7ed\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2d26eae elementor-position-top elementor-widget elementor-widget-image-box\" data-id=\"2d26eae\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;}\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 25-04-2024 *\/\n.elementor-widget-image-box .elementor-image-box-content{width:100%}@media (min-width:768px){.elementor-widget-image-box.elementor-position-left .elementor-image-box-wrapper,.elementor-widget-image-box.elementor-position-right .elementor-image-box-wrapper{display:flex}.elementor-widget-image-box.elementor-position-right .elementor-image-box-wrapper{text-align:end;flex-direction:row-reverse}.elementor-widget-image-box.elementor-position-left .elementor-image-box-wrapper{text-align:start;flex-direction:row}.elementor-widget-image-box.elementor-position-top .elementor-image-box-img{margin:auto}.elementor-widget-image-box.elementor-vertical-align-top .elementor-image-box-wrapper{align-items:flex-start}.elementor-widget-image-box.elementor-vertical-align-middle .elementor-image-box-wrapper{align-items:center}.elementor-widget-image-box.elementor-vertical-align-bottom .elementor-image-box-wrapper{align-items:flex-end}}@media (max-width:767px){.elementor-widget-image-box .elementor-image-box-img{margin-left:auto!important;margin-right:auto!important;margin-bottom:15px}}.elementor-widget-image-box .elementor-image-box-img{display:inline-block}.elementor-widget-image-box .elementor-image-box-title a{color:inherit}.elementor-widget-image-box .elementor-image-box-wrapper{text-align:center}.elementor-widget-image-box .elementor-image-box-description{margin:0}<\/style><div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/05\/26\/le-serverless-une-approche-efficace-a-bas-prix\/\" tabindex=\"-1\"><img decoding=\"async\" width=\"2560\" height=\"1576\" src=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless.jpeg\" class=\"attachment-full size-full wp-image-3177\" alt=\"\" srcset=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless.jpeg 2560w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless-300x185.jpeg 300w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless-1024x630.jpeg 1024w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless-768x473.jpeg 768w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless-1536x945.jpeg 1536w, https:\/\/datapy.fr\/wp-content\/uploads\/2021\/05\/DataPy_Article_moteur_de_requetes_pour_dashboarding_bigdata_serverless-2048x1261.jpeg 2048w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/05\/26\/le-serverless-une-approche-efficace-a-bas-prix\/\">\u201cBuzz Query\u201d, un moteur de requ\u00eates pour dashboarding bigdata serverless<\/a><\/h3><p class=\"elementor-image-box-description\">L\u2019histoire commence par la rencontre entre l\u2019\u00e9quipe DataPy et R\u00e9mi Dettai\u2026 R\u00e9mi est un ing\u00e9nieur de talent sp\u00e9cialis\u00e9 en Data et Cloud. R\u00e9mi a eu de nombreuses exp\u00e9riences dans les domaines de la tech, chez des industriels com...<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-31487be\" data-id=\"31487be\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-59b1e94 elementor-position-top elementor-widget elementor-widget-image-box\" data-id=\"59b1e94\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/06\/07\/forces-et-faiblesses-de-spark-et-elasticsearch\/\" tabindex=\"-1\"><img decoding=\"async\" width=\"1050\" height=\"566\" src=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/20210604-DataPy_Apache_SPARK_ELASTICSEARCH-e1711553094473.jpeg\" class=\"attachment-full size-full wp-image-3184\" alt=\"\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/06\/07\/forces-et-faiblesses-de-spark-et-elasticsearch\/\">Forces et faiblesses de Spark et Elasticsearch<\/a><\/h3><p class=\"elementor-image-box-description\">\nNous avons partag\u00e9 les enjeux li\u00e9s \u00e0 la techno du Serverless dans le pr\u00e9c\u00e9dent article. Nous allons creuser aujourd\u2019hui les solutions les plus connues dans le domaine du traitement de donn\u00e9es \u00e0 grande \u00e9chelle, Spark et Elasticsearch.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-e385686\" data-id=\"e385686\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3529281 elementor-position-top elementor-widget elementor-widget-image-box\" data-id=\"3529281\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/05\/26\/le-serverless-une-approche-efficace-a-bas-prix\/\" tabindex=\"-1\"><img loading=\"lazy\" decoding=\"async\" width=\"294\" height=\"186\" src=\"https:\/\/datapy.fr\/wp-content\/uploads\/2021\/06\/Capture-decran-2023-03-01-a-16.05.04.png\" class=\"attachment-full size-full wp-image-3360\" alt=\"\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/datapy.fr\/index.php\/2021\/05\/26\/le-serverless-une-approche-efficace-a-bas-prix\/\">Le serverless, une approche efficace \u00e0 bas prix<\/a><\/h3><p class=\"elementor-image-box-description\"> \nNous avions introduit dans l\u2019article pr\u00e9c\u00e9dent ce qu\u2019\u00e9tait la notion de moteur de requ\u00eate de dashboarding. Nous allons \u00e0 pr\u00e9sent nous concentrer sur une nouvelle notion, plus proche des ressources mat\u00e9rielles, le serverless.\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Athena, solution serverless d\u2019Amazon, mise en perspective de \u201cBuzz Query\u201d, moteur de requ\u00eates de dashboarding serverless bigdata Nous avions explor\u00e9 dans l\u2019article pr\u00e9c\u00e9dent les forces et faiblesses de Spark et Elasticsearch. Nous allons \u00e0 pr\u00e9sent creuser en quoi la solution Athena d\u2019Amazon est une brique pertinente, notamment quand elle est int\u00e9gr\u00e9e dans une solution comme [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3186,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-633","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","entry","has-media"],"_links":{"self":[{"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/posts\/633","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/comments?post=633"}],"version-history":[{"count":0,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/posts\/633\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/media\/3186"}],"wp:attachment":[{"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/media?parent=633"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/categories?post=633"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datapy.fr\/index.php\/wp-json\/wp\/v2\/tags?post=633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}