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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-15982ca3c6",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bb00202b5d","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-ca0ce8d077","text":"\u003Cp\u003ELes entreprises génèrent d’énormes quantités de données chaque jour, mais à l’état brut, leur valeur réelle n’est pas à la hauteur de leurs promesses. Que ce soit lors de l’élaboration d’une recette ou de la construction d’un produit sur une chaîne de montage, le résultat final est la somme de ses parties, toutes réunies pour créer quelque chose d’utile (ou de délicieux). De la même façon, lorsqu’une entreprise dispose d’un amas de données brutes, elle doit d’abord les organiser pour les rendre utilisables. Pour ce faire, il faut suivre une série d’étapes : le traitement de données.\u003C/p\u003E\n\u003Cp\u003ELe traitement de données consiste à structurer des données brutes et souvent désordonnées en un format utile. Grâce à une série d’opérations, les entreprises mettent au jour la valeur cachée dans des colonnes de chiffres, des pages de réponses à des enquêtes et des feuilles de calcul bourrées d’informations. Ce processus est au cœur de toute stratégie commerciale. Avec lui, tout devient possible, de l’analyse stratégique au machine learning (ML).\u003C/p\u003E\n\u003Cp\u003EDans ce guide, nous verrons en quoi consiste le traitement de données, son importance, ainsi que les étapes, types, méthodes, outils et technologies de traitement de données. Nous terminerons par quelques questions fréquentes sur cette opération stratégique.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-21a778b3f9","additionalClasses":"headline-decoration","type":"heading2","lines":["En quoi consiste le traitement de données ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4d9d696d4c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELe traitement de données est une série systématique d’opérations qui prend des données brutes et désorganisées pour les transformer en informations exploitables et significatives sur lesquelles les entreprises peuvent se fonder pour prendre des décisions éclairées. Essentiel pour permettre \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-analytics/\"\u003El’analyse des données\u003C/a\u003E, il s’agit d’un élément fondamental de la stratégie commerciale.\u003C/p\u003E\r\n\u003Cp\u003EHistoriquement, le traitement de données était un processus manuel particulièrement laborieux et chronophage. Des calculateurs humains (c’est-à-dire des personnes qui occupaient le \u003Ca href=\"https://www.nasa.gov/centers-and-facilities/jpl/when-computers-were-human/\"\u003Eposte de « calculateur »\u003C/a\u003E), utilisaient des outils physiques tels que des registres, des formulaires et des calculatrices, ainsi que des systèmes sur papier, pour collecter, stocker et analyser des données. \u003Ca href=\"https://www.rudderstack.com/learn/data-collection/data-collection-history/\"\u003ED’après une anecdote célèbre\u003C/a\u003E, il fallut \u003Ci\u003Esept ans\u003C/i\u003E aux États-Unis pour publier les résultats du recensement de 1880 en raison de la lenteur des processus manuels de comptage, ce qui conduisit Herman Hollerith, un employé du Bureau du recensement des États-Unis, à inventer la machine à calculer. Cette invention a considérablement réduit le temps nécessaire au traitement des données de recensement, le faisant passer de plusieurs années à quelques mois. En outre, elle a jeté les bases du secteur moderne du traitement de données.\u003C/p\u003E\r\n\u003Cp\u003EAujourd’hui, le traitement de données est un processus électronique géré par des ordinateurs et l’automatisation, généralement sous la supervision de data analysts, data processors, \u003Ca href=\"https://www.snowflake.com/fr/product/data-engineering/\"\u003Edata engineers\u003C/a\u003E et data scientists. \u003Ca href=\"https://www.snowflake.com/fr/product/ai/\"\u003EL’IA et le ML\u003C/a\u003E jouent un rôle significatif dans la gestion des jeux de données particulièrement volumineux. Le traitement de données est souvent décrit comme un cycle qui suit un certain nombre d’étapes pour transformer les données à l’état brut afin de permettre leur analyse, leur interprétation, puis leur stockage.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__714116681":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6384173a04","additionalClasses":"headline-decoration","type":"heading2","lines":["Pourquoi le traitement de données est-il important ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-09b3fb348c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESans le traitement de données, les grandes quantités de données que les entreprises génèrent chaque seconde ne seraient rien d’autre que du bruit numérique. Le traitement de données comble le fossé entre les données non traitées qui sont rarement utiles à l’état brut et les informations exploitables clés qui peuvent éclairer les décisions stratégiques et donner aux entreprises un avantage concurrentiel.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EMeilleure prise de décision : \u003C/b\u003Eles entreprises ne peuvent pas s’appuyer sur des hypothèses et des suppositions si elles veulent rester compétitives et se développer. Les informations claires qu’elles obtiennent grâce au traitement de données leur permet d’améliorer la prise de décision de plusieurs manières, notamment grâce à :\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EL’identification des tendances du marché :\u003C/b\u003E les données sur les ventes peuvent fournir des informations sur les produits qui se vendent bien, les groupes démographiques qui les achètent, à quelle période de l’année, etc.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDes gains d’efficacité opérationnelle :\u003C/b\u003E l’analyse des données sur la supply chain, la logistique et la production peut aider les entreprises à identifier les gaspillages et les goulots d’étranglement et à optimiser leurs processus.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDes prédictions basées sur des données :\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/predictive-analytics/\"\u003El’analyse prédictive\u003C/a\u003E s’appuie sur des données historiques pour prévoir des résultats, afin d’aider les entreprises à anticiper les besoins de leurs clients, à gérer leurs stocks et à réduire les risques.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003EUne précision et une fiabilité améliorées :\u003C/b\u003E les données non traitées contiennent très souvent des erreurs, des doublons et des incohérences. Or, dans de nombreux secteurs (\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/governance-risk-compliance/\"\u003Egouvernance, gestion des risques et conformité\u003C/a\u003E, détection des fraudes, services financiers, etc.), la moindre erreur ou divergence peut faire boule de neige et entraîner des complications encore plus graves. Dans le cadre du traitement de données (que nous expliquerons bientôt), l’étape du nettoyage des données identifie et corrige ces problèmes, afin que les données soient plus précises et fiables au moment de leur analyse.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EUn plus grand avantage concurrentiel :\u003C/b\u003E les entreprises qui traitent et exploitent efficacement leurs données bénéficient d’un facteur de différentiation clé pour prendre une longueur d’avance sur leurs concurrents. Voici quelques-uns des avantages qu’elles s’octroient ainsi :\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPersonnalisation des expériences clients :\u003C/b\u003E le traitement des données clients permet aux entreprises de proposer à leurs clients des recommandations personnalisées, un marketing ciblé et des services susceptibles de les intéresser, ce qui renforce leur fidélité à la marque.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERéactivité face aux changements du marché :\u003C/b\u003E le traitement de données en temps réel permet aux entreprises de réagir rapidement aux évolutions du marché, qu’il s’agisse d’un concurrent qui lance un nouveau produit ou d’un revirement de la demande client.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003EAmélioration de la sécurité et de la conformité des données :\u003C/b\u003E le traitement de données vise non seulement à rendre les données utiles, mais aussi à les sécuriser. Des \u003Ca href=\"https://www.acceldata.io/blog/data-compliance\"\u003Emesures de protection spécifiques\u003C/a\u003E sont intégrées dans le processus de nettoyage et d’organisation des données, notamment le \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Emasquage des données\u003C/a\u003E, l’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003Eanonymisation\u003C/a\u003E, le chiffrement et la tokénisation. Les systèmes de traitement de données appliquent également des règles pour encadrer qui peut accéder aux données, les modifier ou les supprimer. En outre, de nombreuses réglementations sur les données, comme le RGPD et l’HIPAA, imposent des exigences strictes concernant les données qui peuvent être collectées, leur utilisation, etc. Des systèmes de traitement de données documentent méticuleusement chaque étape du cycle de vie des données, de leur collecte à leur suppression ; ils créent ainsi une piste d’audit qui prouve qu’une entreprise respecte les réglementations.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-46478a54ad","additionalClasses":"headline-decoration","type":"heading2","lines":["Étapes du traitement de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ad6ff24dba","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EPrécédemment dans ce guide, nous avons comparé le traitement de données à une chaîne de montage en usine. Tout comme la construction d’une voiture qui passe par différentes étapes (du martelage du châssis à la peinture et au polissage finaux), le traitement de données suit un flux de travail structuré en plusieurs étapes. Chaque étape est essentielle pour convertir des données brutes et désordonnées en données propres et fiables sur lesquelles les entreprises peuvent s’appuyer pour prendre des décisions éclairées et élaborer des stratégies efficaces.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Collecte\u003C/h4\u003E\n\u003Cp\u003EIl est temps de commencer à recueillir ces données, qui peuvent provenir d’une multitude de sources : logs de transactions, bases de données d’entreprise, statistiques sur l’engagement sur les réseaux sociaux, enquêtes auprès des clients, etc. Elles sont souvent hébergées dans des \u003Ca href=\"https://www.snowflake.com/fr/product/analytics/\"\u003Edata lakes et des data warehouses\u003C/a\u003E. Il est essentiel que les données extraites au cours de cette première étape soient pertinentes, exactes et de sources fiables. Dans le cas contraire, les résultats finaux risquent d’être biaisés, ce qui compromet complètement le projet dès le départ.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Préparation\u003C/h4\u003E\n\u003Cp\u003ESouvent appelée prétraitement, cette étape est la plus stratégique et chronophage. Elle consiste à nettoyer et organiser les données pour s’assurer de leur qualité et de leur cohérence. Ces étapes comprennent :\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ENettoyage des données : \u003C/b\u003Ecorrection des erreurs, remplissage des valeurs manquantes, suppression des données en double ou non pertinentes.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003ETransformation des données \u003C/a\u003E:\u003C/b\u003E conversion des données dans un format homogène (normalisation du format des dates, conversion du texte en code numérique, etc.).\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EValidation des données :\u003C/b\u003E vérification de la conformité des données à certaines règles pour s’assurer de leur exactitude.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.fullstory.com/blog/data-enrichment/\"\u003EEnrichissement des données\u003C/a\u003E : \u003C/b\u003Eamélioration du jeu de données avec des informations pertinentes supplémentaires provenant de sources externes.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E\u003Cbr\u003E\n3. Entrée\u003C/h4\u003E\n\u003Cp\u003EÀ ce moment-là, les données préparées sont ingérées dans le système de traitement. C’est la première étape où les données brutes commencent à prendre la forme de données utilisables. Par exemple, un système de traitement peut être un logiciel ou un algorithme conçu pour des types de données ou des objectifs d’analyse spécifiques, comme \u003Ca href=\"https://www.ibm.com/think/topics/apache-spark\"\u003EApache Spark\u003C/a\u003E pour de grands jeux de données. À cette étape, la saisie manuelle (pour les petits jeux de données), l’importation de données à partir de sources externes ou la capture automatique de données sont autant de méthodes d’entrée des données dans ces systèmes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. Traitement\u003C/h4\u003E\n\u003Cp\u003EComme son nom l’indique, cette étape est au cœur même du cycle de traitement de données. Différentes techniques sont utilisées pour transformer les données en informations significatives, en fonction du résultat souhaité ou des informations à extraire des données. Elles incluent :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ETri : \u003C/b\u003Eorganisation des données dans un ordre spécifique.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFiltrage :\u003C/b\u003E sélection de sous-ensembles de données spécifiques.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ECalcul : \u003C/b\u003Eexécution d’opérations mathématiques, par exemple pour calculer des totaux ou des moyennes.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAgrégation :\u003C/b\u003E synthèse des données issues de plusieurs enregistrements.\u003Cbr\u003E\n \u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E5. Sortie et interprétation\u003C/h4\u003E\n\u003Cp\u003EAprès leur traitement, les données sont présentées dans un format digeste et facile à comprendre. La sortie désigne le produit final, qui peut être un graphique, un tableau de bord ou toute autre représentation visuelle. La phase d’interprétation consiste en l’analyse de cette sortie afin d’en tirer des conclusions, d’identifier des tendances et de prendre des décisions éclairées. C’est là que les données traitées offrent enfin toute leur valeur.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E6. Stockage\u003C/h4\u003E\n\u003Cp\u003ELa dernière étape consiste à stocker en toute sécurité les données traitées dans des bases de données ou des data warehouses, afin de pouvoir les récupérer et les utiliser ultérieurement. Cette étape est cruciale pour plusieurs raisons :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EAudit et conformité :\u003C/b\u003E un registre est créé à des fins légales et réglementaires.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnalyse future :\u003C/b\u003E les données peuvent être utilisées comme base pour des analyses plus complexes.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ERéférence :\u003C/b\u003E une source fiable d’informations historiques est créée pour soutenir la prise de décision.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-5ccceb57a6","additionalClasses":"headline-decoration","type":"heading2","lines":["Types de traitement de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2f66ac9513","text":"\u003Cp\u003EDiverses méthodes sont utilisées pour transformer des données brutes en informations pertinentes et exploitables. Bien qu’il en existe beaucoup (chacune étant particulièrement adaptée à différents scénarios et exigences), voici trois des méthodes de traitement les plus courantes : par batch, en temps réel et en ligne.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Traitement par batch\u003C/h4\u003E\n\u003Cp\u003ELe \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/streaming-data-pipelines/\"\u003Etraitement par batch\u003C/a\u003E est une méthode qui consiste à collecter un grand volume de données sur une certaine période, puis à les traiter toutes ensemble. Cette approche est idéale pour les tâches qui ne sont pas urgentes et qui peuvent être planifiées pendant les heures creuses afin de ménager les ressources informatiques. Parmi les cas d’usage idéaux, on retrouve les systèmes de paie, la facturation mensuelle, les rapports de fin de journée ou encore la génération de relevés bancaires. Par exemple, une société de cartes de crédit peut collecter toutes les transactions tout au long de la journée et les traiter en un seul batch pendant la nuit afin de mettre à jour les comptes clients.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Traitement en temps réel\u003C/h4\u003E\n\u003Cp\u003ELe traitement en temps réel s’occupe des données dès leur génération, afin de fournir des résultats immédiats. Cette méthode est essentielle quand il faut passer instantanément de l’entrée des données à la sortie, en particulier pour les systèmes où le moindre retard peut avoir des conséquences graves. Ainsi, ce type de traitement de données est notamment utilisé pour \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Ela détection des fraudes dans les transactions financières\u003C/a\u003E, les systèmes GPS et les systèmes de contrôle aérien.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Traitement en ligne\u003C/h4\u003E\n\u003Cp\u003ELe \u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Etraitement en ligne\u003C/a\u003E est un type de traitement en temps réel interactif. Il traite les transactions initiées par les utilisateurs au fur et à mesure, afin de leur fournir une réponse immédiate. Nous en faisons l’expérience au quotidien lorsque nous interagissons avec des sites web et des applications. En résumé, un utilisateur lance une requête ou saisit des données, et le système les traite immédiatement pour y répondre. Ces systèmes sont toujours en ligne et prêts à traiter les demandes des utilisateurs à tout moment. L’e-commerce, les banques en ligne, les réservations aériennes et les jeux en ligne utilisent tous le traitement en ligne. Avez-vous déjà acheté des tickets de concert ou de cinéma en ligne ? C’est ainsi que votre paiement est traité et que le système est immédiatement mis à jour pour que personne d’autre ne puisse acheter la même place.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__987904085":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-116d89604b","additionalClasses":"headline-decoration","type":"heading2","lines":["Méthodes de traitement de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-af83cf9fd1","text":"\u003Cp\u003EIl existe différentes méthodes de traitement de données et toutes ne sont pas compatibles avec tous les types de traitement.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Traitement de données manuel\u003C/h4\u003E\n\u003Cp\u003EIl s’agit de la méthode de traitement de données la plus ancienne et la plus traditionnelle. Elle consiste à collecter, organiser et analyser des données entièrement à la main, sans l’aide de machines. Lente et propice aux erreurs, cette méthode nécessite beaucoup de main-d’œuvre et ne se prête pas bien au traitement de gros volumes de données. En revanche, il s’agit d’une option adaptée aux opérations ou entreprises à petite échelle ou pour lesquelles le bon sens humain est essentiel, comme pour le recomptage manuel des bulletins de vote pendant une élection.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Traitement de données mécanique\u003C/h4\u003E\n\u003Cp\u003ESi vous utilisez des machines et des appareils simples pour traiter des données (comme des calculatrices, des machines à écrire ou des machines à cartes perforées), vous utilisez la méthode mécanique de traitement de données. La machine à calculer Hollerith dont nous avons déjà parlé dans ce guide en constitue un exemple. Le traitement de données mécanique est idéal pour les tâches simples. Elle génère moins d’erreurs que le traitement de données manuel, mais elle non plus n’est pas idéale pour traiter des jeux de données volumineux.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Traitement de données électronique\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.lenovo.com/us/en/glossary/what-is-edp/?orgRef=https%253A%252F%252Fwww.google.com%252F&srsltid=AfmBOooBYFjcgNsJVG7eEUuzaIdrjkhzOJ_4Z3st5mhFK4tMT58CPgpa\"\u003ELe traitement de données électronique (EDP)\u003C/a\u003E est la méthode la plus moderne et la plus utilisée. Il s’appuie sur des solutions électroniques telles que des ordinateurs, des serveurs et l’automatisation pour traiter des données. Il s’agit d’une approche hautement efficace, précise et évolutive, capable de traiter d’énormes quantités de données en temps réel. L’EDP automatise l’ensemble du cycle de traitement de données, de l’entrée à la sortie, et est aujourd’hui utilisé dans pratiquement tous les secteurs, des systèmes de paie simples aux applications de Big Data.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__714116681":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-98c88c50b5","additionalClasses":"headline-decoration","type":"heading2","lines":["Outils et technologies de traitement de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-49daa46a02","text":"\u003Cp\u003ELe traitement de données moderne repose sur une combinaison d’outils puissants et de technologies émergentes pour extraire des informations précieuses à partir de données brutes non traitées. Ces solutions prennent en charge tous les types de tâches, du stockage de données de base aux analyses complexes et automatisées. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1. Bases de données et data warehouses\u003C/h4\u003E\r\n\u003Cp\u003EIl s’agit d’outils fondamentaux pour le stockage et la gestion des données, mais il \u003Ca href=\"https://www.coursera.org/articles/data-warehouse-vs-database\"\u003En’ont pas la même fonction\u003C/a\u003E dans le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/modernizing-data-pipelines/\"\u003Epipeline de traitement\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELes bases de données\u003C/b\u003E servent à stocker et à organiser des informations issues d’une source de données unique pour une fonction particulière de votre entreprise. Considérez-les comme des classeurs méticuleusement organisés dans un seul but. Ils sont conçus pour exécuter des tâches rapides et fréquentes, ainsi que des petites requêtes. Parmi les bases de données populaires figurent des systèmes basés sur SQL tels que MySQL, PostgreSQL ou encore Microsoft SQL Server.\u003C/p\u003E\r\n\u003Cp\u003EÀ l’inverse, les \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Edata warehouses\u003C/a\u003E sont de grands référentiels centralisés conçus pour stocker de grandes quantités de données historiques provenant de sources multiples. Destinés à l’analyse, ils servent essentiellement de bibliothèques dans lesquelles les data analysts vont chercher des informations afin de répondre à des questions sur des tendances stratégiques complexes. Ils sont conçus pour exécuter des requêtes complexes sur de grands jeux de données à des fins de génération de rapports et de Business Intelligence. Les \u003Ca href=\"https://www.snowflake.com/fr/product/platform/\"\u003Edata warehouses\u003C/a\u003E utilisent souvent des technologies de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/big-data/\"\u003EBig Data\u003C/a\u003E comme Snowflake, Hadoop et Apache Spark, ainsi que des \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2. Intelligence artificielle et machine learning\u003C/h4\u003E\r\n\u003Cp\u003EL’IA et le ML sont des technologies puissantes qui automatisent et améliorent chaque étape du traitement de données. Ces technologies vont au-delà de la gestion de calculs simples pour découvrir des tendances et faire des prédictions. L’IA peut automatiser le nettoyage et la préparation des données, mais aussi détecter et corriger automatiquement les erreurs, renseigner les valeurs manquantes et standardiser les formats de données. Lorsque des modèles de ML sont entraînés sur des données historiques, ils peuvent faire des prédictions, repérer des anomalies et segmenter des données.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E3. Technologies cloud et plateformes d’analyse de données\u003C/h4\u003E\r\n\u003Cp\u003ELes fournisseurs cloud tels qu’Amazon Web Services (AWS), Google Cloud Platform (GCP) et Microsoft Azure permettent aux entreprises d’adapter leurs ressources de traitement de données à la hausse ou à la baisse en fonction de leurs besoins, sans avoir à acheter ou à entretenir du matériel on-premise coûteux. Ils permettent également le traitement du Big Data à grande échelle, que la plupart des entreprises ne pourraient tout simplement pas prendre en charge sans eux.\u003C/p\u003E\r\n\u003Cp\u003ELes plateformes d’analyse de données sont des solutions logicielles souvent basées sur le cloud qui fournissent un environnement complet pour le traitement de données. Snowflake et Tableau offrent une plateforme unifiée pour stocker des données, exécuter des requêtes analytiques, créer des \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-visualization/\"\u003Evisualisations\u003C/a\u003E et simplifier des flux de travail complexes. Par exemple, avec \u003Ca href=\"https://www.snowflake.com/fr/product/platform/\"\u003El’AI Data Cloud de Snowflake\u003C/a\u003E, les données chargées sur la plateforme sont optimisées pour des opérations hautes performances et sont compatibles avec les principaux clouds publics.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-dbb9f3a5d3","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusion"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ac22a3219d","text":"\u003Cp\u003ELe traitement de données est le moteur indispensable qui alimente la transformation des données brutes et désorganisées, afin de fournir aux entreprises les informations stratégiques dont elles ont besoin pour prendre des décisions éclairées. Notre monde a bien changé depuis l’époque où la journalisation et l’analyse de données se faisaient à la main. Nous disposons désormais de solutions puissantes et automatisées basées sur l’IA et le ML pour gérer les énormes quantités de données que les entreprises produisent de nos jours et qui continuent de croître de manière exponentielle. Un traitement de données efficace et intelligent est plus important que jamais pour tirer du sens de la nuée de données que les entreprises génèrent chaque jour afin d’assurer leur croissance et leur succès futurs.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__127842237":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0ff41db83c","additionalClasses":"headline-decoration","type":"heading2","lines":["Questions fréquentes sur le traitement de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-194fee0127","showDivider":false,"accordionItemsList":[{"title":"Quels sont les types de logiciels disponibles pour le traitement du Big Data ?","richText":"\u003Cp\u003E\u003Cb\u003EFrameworks de calcul distribué :\u003C/b\u003E Apache Hadoop, Apache Spark\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EData warehouses dans le cloud :\u003C/b\u003E Google BigQuery, Amazon Redshift, Microsoft Azure HDInsight\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EBases de données NoSQL :\u003C/b\u003E MongoDB, Apache Cassandra\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ESystèmes de traitement de streams : \u003C/b\u003EApache Flink, Apache Storm\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EOutils de Business Intelligence (BI) et de visualisation : \u003C/b\u003ETableau, Microsoft Power BI\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EPlateformes data intégrées :\u003C/b\u003E Snowflake\u003C/p\u003E\n"},{"title":"Quelles sont les sources de données couramment utilisées pour le Big Data ?","richText":"\u003Cp\u003ELe Big Data regroupe des données d’une grande variété de sources, qui peuvent être globalement classées en trois types : \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Eles données structurées, non structurées et semi-structurées\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EStructurées :\u003C/b\u003E données très organisées qui suivent un format prédéterminé. Généralement stockées dans des tables, ce sont donc les données les plus faciles à trouver, à gérer et à analyser avec des outils traditionnels. Exemples : transactions financières, données de points de vente, dossiers médicaux.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ENon structurées :\u003C/b\u003E pas de format prédéfini. Ce sont les données les plus courantes du Big Data, mais aussi les plus difficiles à analyser. Elles comprennent du texte, des images, de l’audio et des vidéos. Exemples : données sur les réseaux sociaux, PDF et e-mails, données de capteurs de thermostats intelligents ou d’appareils portables.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003ESemi-structurées\u003C/a\u003E :\u003C/b\u003E données hybrides entre les deux autres types. Elles n’ont pas la structure rigide des données structurées, mais possèdent certaines propriétés organisationnelles qui les rendent plus faciles à catégoriser et à analyser que les données non structurées. Exemples : fichiers XML et JSON, fichiers de logs, pages web.\u003C/p\u003E\n"},{"title":"Quels exemples de traitement de données pouvons-nous citer ?","richText":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ETraitement de la paie : \u003C/b\u003Edes données sur les collaborateurs, y compris les heures travaillées, les retenues, les salaires et les renseignements fiscaux, sont utilisées pour calculer et émettre les chèques de paie à temps.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ERecommandations pour le e-commerce :\u003C/b\u003E lorsque vous naviguez sur une boutique en ligne, les systèmes de cette entreprise traitent votre historique de recherche, vos achats passés et d’autres données pour vous recommander des produits qui pourraient vous plaire.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPrévisions météorologiques :\u003C/b\u003E les météorologues traitent une grande quantité de données provenant de satellites, de capteurs au sol et de stations météorologiques pour créer des modèles complexes qui prédisent les tendances météorologiques et émettent des prévisions (même si nous ne sommes pas toujours convaincus de leur précision).\u003C/li\u003E\n\u003C/ul\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1190438074":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-69e6b6ef74","additionalClasses":"headline-decoration","type":"heading2","lines":["Clients qui utilisent l’AI Data Cloud pour le traitement de données "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-a91fbe15f8","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-192a0c22e3",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-415323fac5","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Luminate optimise l’analytique dans le divertissement avec un traitement des données 300 % plus rapide et des informations plus 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