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aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-c81148278c","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/tasty-bytes-zero-to-snowflake-semi-structured-data-fr",":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroTitle":{"lines":["Tasty Bytes – Débuter avec Snowflake – Données semi-structurées"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Jacob 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Powered by Tasty Bytes Débuter avec Snowflake sur le traitement des données semi-structurées !\u003C/p\u003E\n","\u003Cp\u003EDans ce guide Quickstart, vous allez apprendre à traiter des données semi-structurées dans Snowflake. Vous allez notamment découvrir le type de données VARIANT, comment traiter des données semi-structurées via la notation par points et la fonction d’aplatissement avec vue latérale, comment créer des vues et comment utiliser des graphiques Snowsight.\u003C/p\u003E\n","\u003Cp\u003EPour plus d’informations au sujet des données semi-structurées dans Snowflake, consultez l’article de documentation intitulé \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/semistructured-concepts\"\u003EDonnées semi-structurées\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EConditions préalables\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAvant de commencer, assurez-vous de suivre l’intégralité du guide Quickstart \u003Ca href=\"/fr/developers/guides/tasty-bytes-introduction-fr/\"\u003E\u003Cstrong\u003EPrésentation de Tasty Bytes\u003C/strong\u003E\u003C/a\u003E, qui explique comment configurer un compte d’essai et déployer les éléments de base de Tasty Bytes requis pour suivre ce guide Quickstart.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVous allez apprendre :\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EQu’est-ce que le type de données VARIANT\u003C/li\u003E\u003Cli\u003EComment utiliser la notation par points pour interroger des données semi-structurées\u003C/li\u003E\u003Cli\u003EComment aplatir des données semi-structurées\u003C/li\u003E\u003Cli\u003EComment analyser des tableaux (ARRAY)\u003C/li\u003E\u003Cli\u003EComment créer une vue\u003C/li\u003E\u003Cli\u003EComment créer un graphique à partir d’un jeu de résultats dans Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVous allez créer :\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUn processus de données semi-structurées de bout en bout\u003C/li\u003E\u003Cli\u003EUne vue harmonisée (Silver) et d’analyse (Gold)\u003C/li\u003E\u003Cli\u003EUn graphique à barres digne d’un cadre\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECréer une feuille de calcul et la copier dans un fichier SQL\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EDans ce guide Quickstart, vous allez suivre une histoire de Tasty Bytes via une feuille de calcul SQL Snowsight. Cette page, qui fait office de guide étape par étape, comprend également des commentaires, des images et des liens vers de la documentation.\u003C/p\u003E\n","\u003Cp\u003ECette section vous explique comment vous connecter à Snowflake, comment créer une nouvelle feuille de calcul et la renommer, mais aussi comment copier le fichier SQL que nous allons utiliser dans ce guide Quickstart depuis GitHub et le coller.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Accéder à Snowflake via une URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EOuvrez une fenêtre de votre navigateur et saisissez l’URL de votre compte Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 2 – Se connecter à Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EConnectez-vous à votre compte Snowflake.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/log_into_snowflake.gif\" alt=\"assets/log_into_snowflake.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 3 – Accéder aux feuilles de calcul\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECliquez sur l’onglet Worksheets (Feuilles de calcul) dans la barre de navigation de gauche.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/worksheet_tab.png\" alt=\"assets/worksheet_tab.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 4 – Créer une feuille de calcul\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESous l’onglet Worksheets (Feuilles de calcul), cliquez sur le bouton « + » dans le coin supérieur droit de Snowsight, puis sélectionnez « SQL Worksheet » (Feuille de calcul SQL).\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/+_sqlworksheet.png\" alt=\"assets/+_sqlworksheet.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 5 – Renommer une feuille de calcul\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERenommez la feuille de calcul en cliquant sur le nom généré automatiquement (horodatage), puis en saisissant "Tasty Bytes – Données semi-structurées".\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/rename_worksheet_tasty_bytes_setup.gif\" alt=\"assets/rename_worksheet_tasty_bytes_setup.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 6 – Accéder au fichier SQL de ce guide Quickstart dans GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECliquez sur le bouton ci-dessous pour être redirigé vers votre fichier SQL Tasty Bytes hébergé sur GitHub. <button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql\"\u003Etb_zts_semi_structured_data.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 7 – Copier le fichier de configuration SQL depuis GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDans GitHub, à droite, cliquez sur « Copy raw contents » (Copier le contenu brut). L’ensemble du fichier SQL requis est copié dans votre presse-papiers.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 8 – Coller le fichier de configuration SQL depuis GitHub dans une feuille de calcul Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERevenez dans Snowsight et dans votre nouvelle feuille de calcul, puis collez (\u003Cem\u003ECMD + V pour Mac ou CTRL + V pour Windows\u003C/em\u003E) ce que vous venez de copier dans GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EÉtape 9 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EÉtablir le profil des données de menu semi-structurées\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EEn tant que data engineers de Tasty Bytes, il nous a été demandé d’établir le profil de nos données de menu, qui doit inclure une colonne pour les données semi-structurées. Depuis cette table de menu, nous devons créer une vue de couche d’analyse qui affiche les données relatives aux ingrédients et aux restrictions alimentaires pour nos utilisateurs finaux.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Définir le contexte et interroger la table\u003C/h3\u003E\n","\u003Cp\u003ECommençons par exécuter en même temps les trois premières requêtes qui permettront de:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDéfinir le contexte du rôle sur \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EDéfinir le contexte de l’entrepôt sur \u003Ccode\u003Etasty_de_wh\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EProduire un jeu de résultats avec une requête \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/constructs/top_n\"\u003ETOP\u003C/a\u003E 10 de notre table \u003Ccode\u003Eraw_pos.menu\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer; \nUSE WAREHOUSE tasty_de_wh;\n\nSELECT TOP 10\n    m.truck_brand_name, \n    m.menu_type, m.menu_item_name, \n    m.menu_item_health_metrics_obj \nFROM frostbyte_tasty_bytes.raw_pos.menu m; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.1.menu.png\" alt=\"assets/3.1.menu.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELes résultats renvoyés nous montrent que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E doit correspondre aux données semi-structurées censées contenir les indicateurs que nous devons fournir en aval.\u003C/p\u003E\n","\u003Cp\u003ESi vous cliquez sur l’une des cellules de cette colonne, Snowsight développera automatiquement le volet des statistiques pour vous en donner un meilleur aperçu.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.1.2.stats.png\" alt=\"assets/3.1.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – Explorer la colonne de données semi-structurées\u003C/h3\u003E\n","\u003Cp\u003EPour examiner de plus près comment cette colonne est définie dans Snowflake, exécutez la requête suivante, qui nous permet d’exploiter la commande \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/show-columns\"\u003ESHOW COLUMNS\u003C/a\u003E pour explorer les types de données figurant dans notre table \u003Ccode\u003Emenu\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.2.show_columns.png\" alt=\"assets/3.2.show_columns.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDans notre jeu de résultats, nous constatons que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E est un type de données \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/data-types-semistructured\"\u003EVARIANT\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EPour les données qui sont le plus souvent régulières et n’utilisent que des types de données natifs pour le format semi-structuré que vous utilisez (chaînes et entiers pour le format JSON), les exigences de stockage et les performances de requête pour les opérations sur les données relationnelles et les données dans une colonne VARIANT sont très similaires.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EÉtape 3 – Parcourir des données semi-structurées à l’aide de la notation par points\u003C/h3\u003E\n","\u003Cp\u003EDans notre colonne \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E, nous avons vu que \u003Ccode\u003Emenu_item_id\u003C/code\u003E était inclus avec les données relatives aux ingrédients et aux restrictions alimentaires les plus imbriquées auxquelles nous devons accéder.\u003C/p\u003E\n","\u003Cp\u003EExécutez la requête suivante, qui nous permet de commencer à utiliser la \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/querying-semistructured#dot-notation\"\u003Enotation par points\u003C/a\u003E pour parcourir nos données semi-structurées.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.3.dot.png\" alt=\"assets/3.3.dot.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGrâce à la notation par points, nous avons pu extraire complètement \u003Ccode\u003Emenu_item_id\u003C/code\u003E, mais il semble qu’il reste encore des objets semi-structurés supplémentaires dans la sortie de la colonne \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ECliquons de nouveau sur l’une des cellules de cette colonne pour en examiner le contenu plus en détail.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.3.2.stats.png\" alt=\"assets/3.3.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENous progressons !\u003C/strong\u003E Dans la prochaine section, voyons comment nous pouvons aller plus loin dans le traitement de \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E en utilisant d’autres fonctions Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 4 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EAplatir des données semi-structurées\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EAprès avoir vu comment nous pouvons facilement interroger des données semi-structurées qui figurent dans une colonne VARIANT à l’aide de la notation par points, en tant que data engineer chez Tasty Bytes, vous êtes bien parti pour fournir aux parties prenantes internes les données qu’elles vous ont demandées.\u003C/p\u003E\n","\u003Cp\u003EDans cette section, nous allons procéder à un traitement plus poussé des données semi-structurées afin de répondre à certains besoins.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Découvrir la fonction d’aplatissement avec vue latérale\u003C/h3\u003E\n","\u003Cp\u003ECette fonction permet d’extraire de notre colonne \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E davantage de données demandées par nos utilisateurs en aval. Exécutez la requête suivante, qui utilise la fonctionnalité de notation par points dont nous venons de parler, avec les fonctions d’aplatissement (\u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/flatten\"\u003EFLATTEN\u003C/a\u003E) et de jointure latérale (\u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/constructs/join-lateral\"\u003ELATERAL JOIN\u003C/a\u003E) de Snowflake pour nous fournir le premier ARRAY \u003Ccode\u003Eingredient\u003C/code\u003E qu’on nous a demandé.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFLATTEN :\u003C/strong\u003E fonction de table qui prend une colonne VARIANT, OBJECT ou ARRAY et produit une vue latérale. La fonction FLATTEN peut être utilisée pour convertir des données semi-structurées en une représentation relationnelle.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELATERAL JOIN :\u003C/strong\u003E contrairement à la sortie d’une jointure non latérale, la sortie d’une jointure latérale ne comprend que les lignes générées à partir de la vue en ligne. Les lignes du côté gauche n’ont pas besoin d’être reliées au côté droit, car les lignes du côté gauche ont déjà été prises en compte en étant passées dans la vue en ligne.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.1.lat_flat.png\" alt=\"assets/4.1.lat_flat.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – Explorer une fonction de tableau (ARRAY)\u003C/h3\u003E\n","\u003Cp\u003EAvant que nous procédions à l’extraction des données requises relatives aux restrictions alimentaires, exécutez la requête suivante, à l’aide de la fonction de tableau Snowflake \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/array_contains\"\u003EARRAY_CONTAINS\u003C/a\u003E, qui vous permettra d’explorer la colonne \u003Ccode\u003Eingredients\u003C/code\u003E pour tout \u003Ccode\u003Emenu_item_name\u003C/code\u003E qui contient de la laitue.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:"ingredients"::VARIANT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.2.array_contains.png\" alt=\"assets/4.2.array_contains.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELes résultats renvoyés nous montrent que plusieurs de nos éléments de menu incluent de la laitue. Ce type d’analyse serait très utile aux Procurement Managers de notre chaîne d’approvisionnement en cas de rappels d’aliments dans les villes et pays dans lesquels nous intervenons.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 3 – Structurer des données semi-structurées à grande échelle\u003C/h3\u003E\n","\u003Cp\u003EAprès avoir vu le type de valeur ajoutée que nous pouvons déjà apporter à notre organisation, nous pouvons maintenant exécuter la dernière requête de cette section.\u003C/p\u003E\n","\u003Cp\u003ECette requête utilisera la notation par points, ainsi que notre double fonction de table LATERAL JOIN et FLATTEN, pour fournir le jeu de résultats que l’on nous a initialement demandé de générer.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.3.lat_flat_2.png\" alt=\"assets/4.3.lat_flat_2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EParfait !\u003C/strong\u003E Ces résultats semblent être conformes aux besoins exacts de nos parties prenantes. Dans la section suivante, nous allons voir comment nous pouvons mettre à profit ces résultats dans notre couche d’analyse, où ils peuvent être consultés par nos parties prenantes.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 4 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003ECréer des vues structurées à partir de données semi-structurées\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EDans la section précédente, nous avons créé une requête qui fournit les résultats exacts requis par nos utilisateurs finaux à l’aide d’une suite de fonctionnalités de données semi-structurées Snowflake. Nous allons poursuivre le processus qui consiste à mettre à profit cette requête qui se trouve dans notre couche brute vers la couche harmonisée, puis dans la couche d’analyse, d’où nos utilisateurs finaux sont autorisés à lire des données.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERemarque :\u003C/strong\u003E pour les habitués des modèles de données Bronze, Silver et Gold, ces derniers correspondent, respectivement, aux couches brute, harmonisée et d’analyse.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Créer une vue harmonisée à l’aide de la requête SQL d’aplatissement des données semi-structurées\u003C/h3\u003E\n","\u003Cp\u003EEn reprenant la requête exacte de la fin de la section précédente, exécutez la requête suivante, qui contient cette requête SQL ainsi que toutes les autres colonnes de la table \u003Ccode\u003Emenu\u003C/code\u003E déjà structurées.\u003C/p\u003E\n","\u003Cp\u003EDans cette requête, nous utilisons la commande \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E dans notre schéma harmonisé pour encapsuler la logique de traitement des données semi-structurées et d’autres colonnes sous forme de table.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EUne vue permet de consulter le résultat d’une requête comme s’il s’agissait d’une table. Les vues servent à diverses fins, notamment à combiner, séparer ou protéger des données.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\n    AS\nSELECT \n    m.menu_id,\n    m.menu_type_id,\n    m.menu_type,\n    m.truck_brand_name,\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    m.item_category,\n    m.item_subcategory,\n    m.cost_of_goods_usd,\n    m.sale_price_usd,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/5.1.harmonized_view.png\" alt=\"assets/5.1.harmonized_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELa vue \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E étant créée, nous pourrions désormais l’interroger directement sans avoir à exploiter la requête SQL plus complexe que nous avons utilisée pour la créer. Toutefois, étant donné que la couche d’analyse est celle où nos parties prenantes consultent des données, passons à l’étape suivante pour insérer la requête dans cette couche.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – Utiliser la requête de la couche harmonisée dans la couche d’analyse en toute simplicité\u003C/h3\u003E\n","\u003Cp\u003EÀ partir de la vue \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E, exécutez la requête suivante pour créer notre vue \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E, qui commencera immédiatement à créer de la valeur en aval.\u003C/p\u003E\n","\u003Cp\u003EDans cette requête, nous voyons de nouvelles fonctions dont nous n’avons pas encore parlé. Nous ajoutons tout d’abord une fonction \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E, visible dans les commandes de \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/show\"\u003ESHOW VIEWS\u003C/a\u003E ou dans l’interface Snowsight, pour documenter ce qu’un utilisateur peut voir lorsqu’il interroge cette vue.\u003C/p\u003E\n","\u003Cp\u003ENous utilisons également les paramètres \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE et RENAME\u003C/a\u003E, qui peuvent faciliter la vie des développeurs SQL en simplifiant la définition de la requête ou de la vue.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEXCLUDE :\u003C/strong\u003E lorsque vous sélectionnez toutes les colonnes (SELECT * ou SELECT table_name.*), EXCLUDE spécifie les colonnes à exclure des résultats.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERENAME :\u003C/strong\u003E lorsque vous sélectionnez toutes les colonnes (SELECT * ou SELECT table_name.*), RENAME spécifie les alias de colonnes qui doivent être utilisés dans les résultats.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/5.2.analytics_view.png\" alt=\"assets/5.2.analytics_view.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EÉtape 3 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EAnalyser des données semi-structurées traitées dans Snowsight\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003ENotre vue de menu étant disponible dans notre couche d’analyse, interrogeons-la en exécutant plusieurs requêtes qui montreront à nos utilisateurs finaux comment Snowflake crée une expérience de requête relationnelle à partir de données semi-structurées sans avoir à en faire des copies supplémentaires ou à les traiter de manière complexe.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Analyser des tableaux (ARRAY)\u003C/h3\u003E\n","\u003Cp\u003ELa requête de la section précédente a renvoyé la colonne \u003Ccode\u003Eingredients\u003C/code\u003E, mais celle-ci est désormais disponible sans aucune exigence en matière de notation par points dans notre vue \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EAvec cette vue, vous devez désormais exécuter la requête suivante, qui exploite deux autres fonctions de tableau Snowflake, \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/array_intersection\"\u003EARRAY_INTERSECTION\u003C/a\u003E et \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/arrays_overlap\"\u003EARRAYS_OVERLAP\u003C/a\u003E, pour voir quels éléments de menu hors boissons se chevauchent pour chacun des menus de nos marques de food trucks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m1.menu_type,\n    m1.menu_item_name,\n    m2.menu_type AS overlap_menu_type,\n    m2.menu_item_name AS overlap_menu_item_name,\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\n    ON m1.menu_item_id <> m2.menu_item_id -- avoid joining the same menu item to itself\n    AND m1.menu_type <> m2.menu_type \nWHERE 1=1\n    AND m1.item_category <> 'Beverage' -- remove beverages\n    AND m2.item_category <> 'Beverage' -- remove beverages\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\nORDER BY m1.menu_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.1.arrays.png\" alt=\"assets/6.1.arrays.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe nouveau, en utilisant exactement la vue que nous avons créée, ce type de requête pourrait apporter une véritable valeur commerciale aux opérateurs de nos food trucks, en les aidant à prévoir et commander les ingrédients dont ils ont besoin chaque semaine.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – Fournir des indicateurs aux dirigeants\u003C/h3\u003E\n","\u003Cp\u003EEssayons maintenant d’aider les dirigeants de Tasty Bytes à prendre des décisions au sujet des menus basées sur les données, en leur fournissant des indicateurs globaux relatifs aux restrictions alimentaires que nous gérons actuellement dans l’ensemble de nos marques.\u003C/p\u003E\n","\u003Cp\u003EExécutez la requête suivante, qui utilise les fonctions \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/count\"\u003ECOUNT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/sum\"\u003ESUM\u003C/a\u003E et \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/case\"\u003ECASE\u003C/a\u003E (conditionnelle) pour agréger les indicateurs requis depuis notre vue \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.2.metrics.png\" alt=\"assets/6.2.metrics.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELes résultats que nous venons de recevoir prouvent que nous sommes parvenus à passer d’une table brute contenant des données semi-structurées à une seule ligne agrégée, à laquelle tous les membres de notre organisation peuvent accéder afin d’aider Tasty Bytes à adopter un modèle davantage axé sur les données.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 3 – Convertir des résultats en graphiques\u003C/h3\u003E\n","\u003Cp\u003ECertains des dirigeants de Tasty Bytes préfèrent des représentations visuelles des données. Voyons à quel point il est facile de convertir des résultats renvoyés sous forme de tableau en graphiques visuels faciles à assimiler dans Snowsight.\u003C/p\u003E\n","\u003Cp\u003EAvec la requête SQL que nous avons précédemment utilisée, exécutez la requête suivante, qui ajoute des filtres sur trois noms de nos marques de camionnettes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    m.brand_name,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\nGROUP BY m.brand_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.3.results.png\" alt=\"assets/6.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPar défaut, Snowsight renvoie les résultats des requêtes sous forme de tableau. Cependant, Snowsight contient une fonctionnalité efficace dont nous n’avons pas encore parlé, l’\u003Ca href=\"https://docs.snowflake.com/fr/user-guide/ui-snowsight-visualizations#using-charts\"\u003Eutilisation des graphiques\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESuivez les flèches figurant sur la capture d’écran ci-dessous pour créer un graphique à barres qui compare les éléments de menu de ces différentes marques de food truck concernant certaines restrictions alimentaires.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.3.chart.png\" alt=\"assets/6.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPour terminer ce guide Quickstart, nous devons préciser à quel point il serait facile pour un dirigeant de Tasty Bytes de réaliser lui-même ce type d’analyse sans avoir à connaître la logique de traitement des données semi-structurées que nous avons encapsulée dans les vues que nous avons créées. Nous sommes ainsi sûrs de participer au développement de la démocratisation des données au sein de l’organisation Tasty Bytes.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 4 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusion et étapes suivantes\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EBeau travail ! Vous avez terminé le guide Quickstart Tasty Bytes Débuter avec Snowflake sur les données semi-structurées.\u003C/p\u003E\n","\u003Cp\u003EVous avez :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDécouvert ce qu’est le type de données VARIANT\u003C/li\u003E\u003Cli\u003EUtilisé la notation par points pour interroger des données semi-structurées\u003C/li\u003E\u003Cli\u003EAplati des données semi-structurées\u003C/li\u003E\u003Cli\u003EAnalysé des tableaux (ARRAY)\u003C/li\u003E\u003Cli\u003ECréé deux vues\u003C/li\u003E\u003Cli\u003ECréé un graphique à partir d’un jeu de résultats dans Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESi vous voulez recommencer ce guide Quickstart, utilisez les scripts de réinitialisation en bas de votre feuille de calcul associée.\u003C/p\u003E\n","\u003Ch3\u003EProchaines étapes\u003C/h3\u003E\n","\u003Cp\u003EPour continuer à découvrir le Data Cloud Snowflake, cliquez sur le lien ci-dessous pour voir tous les autres guides Quickstart Powered by Tasty Bytes à votre disposition.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/fr/developers/guides/tasty-bytes-introduction-fr/\"\u003ETable des matières des guides Quickstart Powered by Tasty Bytes\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"title":"Tasty Bytes – Débuter avec Snowflake – Données semi-structurées","elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Traitement de données semi-structurées dans Snowflake\r\n\r\n### Présentation\r\nBienvenue dans le guide Quickstart Powered by Tasty Bytes Débuter avec Snowflake sur le traitement des données semi-structurées !\r\n\r\nDans ce guide Quickstart, vous allez apprendre à traiter des données semi-structurées dans Snowflake. Vous allez notamment découvrir le type de données VARIANT, comment traiter des données semi-structurées via la notation par points et la fonction d’aplatissement avec vue latérale, comment créer des vues et comment utiliser des graphiques Snowsight.\r\n\r\nPour plus d’informations au sujet des données semi-structurées dans Snowflake, consultez l’article de documentation intitulé [Données semi-structurées](https://docs.snowflake.com/fr/user-guide/semistructured-concepts).\r\n\r\n### Conditions préalables\r\n- Avant de commencer, assurez-vous de suivre l’intégralité du guide Quickstart [**Présentation de Tasty Bytes**](/fr/developers/guides/tasty-bytes-introduction-fr/), qui explique comment configurer un compte d’essai et déployer les éléments de base de Tasty Bytes requis pour suivre ce guide Quickstart.\r\n\r\n### Vous allez apprendre :\r\n- Qu’est-ce que le type de données VARIANT\r\n- Comment utiliser la notation par points pour interroger des données semi-structurées\r\n- Comment aplatir des données semi-structurées\r\n- Comment analyser des tableaux (ARRAY)\r\n- Comment créer une vue\r\n- Comment créer un graphique à partir d’un jeu de résultats dans Snowsight\r\n\r\n### Vous allez créer :\r\n- Un processus de données semi-structurées de bout en bout\r\n- Une vue harmonisée (Silver) et d’analyse (Gold)\r\n- Un graphique à barres digne d’un cadre\r\n\r\n## Créer une feuille de calcul et la copier dans un fichier SQL\r\n\r\n### Présentation\r\nDans ce guide Quickstart, vous allez suivre une histoire de Tasty Bytes via une feuille de calcul SQL Snowsight. Cette page, qui fait office de guide étape par étape, comprend également des commentaires, des images et des liens vers de la documentation.\r\n\r\nCette section vous explique comment vous connecter à Snowflake, comment créer une nouvelle feuille de calcul et la renommer, mais aussi comment copier le fichier SQL que nous allons utiliser dans ce guide Quickstart depuis GitHub et le coller.\r\n\r\n### Étape 1 – Accéder à Snowflake via une URL\r\n- Ouvrez une fenêtre de votre navigateur et saisissez l’URL de votre compte Snowflake. \r\n\r\n### Étape 2 – Se connecter à Snowflake\r\n- Connectez-vous à votre compte Snowflake.\r\n    - ![assets/log_into_snowflake.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/log_into_snowflake.gif)\r\n\r\n### Étape 3 – Accéder aux feuilles de calcul\r\n- Cliquez sur l’onglet Worksheets (Feuilles de calcul) dans la barre de navigation de gauche.\r\n    - ![assets/worksheet_tab.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/worksheet_tab.png)\r\n\r\n### Étape 4 – Créer une feuille de calcul\r\n- Sous l’onglet Worksheets (Feuilles de calcul), cliquez sur le bouton « + » dans le coin supérieur droit de Snowsight, puis sélectionnez « SQL Worksheet » (Feuille de calcul SQL).\r\n    - ![assets/+_sqlworksheet.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/+_sqlworksheet.png)\r\n\r\n### Étape 5 – Renommer une feuille de calcul\r\n- Renommez la feuille de calcul en cliquant sur le nom généré automatiquement (horodatage), puis en saisissant \"Tasty Bytes – Données semi-structurées\".\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/rename_worksheet_tasty_bytes_setup.gif)\r\n\r\n### Étape 6 – Accéder au fichier SQL de ce guide Quickstart dans GitHub\r\n- Cliquez sur le bouton ci-dessous pour être redirigé vers votre fichier SQL Tasty Bytes hébergé sur GitHub. \u003Cbutton\u003E[tb_zts_semi_structured_data.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql)\u003C/button\u003E\r\n\r\n### Étape 7 – Copier le fichier de configuration SQL depuis GitHub\r\n- Dans GitHub, à droite, cliquez sur « Copy raw contents » (Copier le contenu brut). L’ensemble du fichier SQL requis est copié dans votre presse-papiers.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/github_copy_raw_contents.png)\r\n\r\n### Étape 8 – Coller le fichier de configuration SQL depuis GitHub dans une feuille de calcul Snowflake\r\n- Revenez dans Snowsight et dans votre nouvelle feuille de calcul, puis collez (*CMD + V pour Mac ou CTRL + V pour Windows*) ce que vous venez de copier dans GitHub.\r\n\r\n### Étape 9 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Établir le profil des données de menu semi-structurées\r\n\r\n### Présentation\r\nEn tant que data engineers de Tasty Bytes, il nous a été demandé d’établir le profil de nos données de menu, qui doit inclure une colonne pour les données semi-structurées. Depuis cette table de menu, nous devons créer une vue de couche d’analyse qui affiche les données relatives aux ingrédients et aux restrictions alimentaires pour nos utilisateurs finaux.\r\n\r\n### Étape 1 – Définir le contexte et interroger la table\r\nCommençons par exécuter en même temps les trois premières requêtes qui permettront de: \r\n- Définir le contexte du rôle sur `tasty_data_engineer` \r\n- Définir le contexte de l’entrepôt sur `tasty_de_wh` \r\n- Produire un jeu de résultats avec une requête [TOP](https://docs.snowflake.com/fr/sql-reference/constructs/top_n) 10 de notre table `raw_pos.menu`\r\n\r\n``` \r\nUSE ROLE tasty_data_engineer; \r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT TOP 10\r\n    m.truck_brand_name, \r\n    m.menu_type, m.menu_item_name, \r\n    m.menu_item_health_metrics_obj \r\nFROM frostbyte_tasty_bytes.raw_pos.menu m; \r\n```\r\n\r\n![assets/3.1.menu.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.1.menu.png)\r\n\r\nLes résultats renvoyés nous montrent que `menu_item_health_metrics_obj` doit correspondre aux données semi-structurées censées contenir les indicateurs que nous devons fournir en aval. \r\n\r\nSi vous cliquez sur l’une des cellules de cette colonne, Snowsight développera automatiquement le volet des statistiques pour vous en donner un meilleur aperçu.\r\n\r\n![assets/3.1.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.1.2.stats.png)\r\n\r\n### Étape 2 – Explorer la colonne de données semi-structurées\r\nPour examiner de plus près comment cette colonne est définie dans Snowflake, exécutez la requête suivante, qui nous permet d’exploiter la commande [SHOW COLUMNS](https://docs.snowflake.com/fr/sql-reference/sql/show-columns) pour explorer les types de données figurant dans notre table `menu`.\r\n\r\n```\r\nSHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\r\n```\r\n\r\n![assets/3.2.show_columns.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.2.show_columns.png)\r\n\r\nDans notre jeu de résultats, nous constatons que `menu_item_health_metrics_obj` est un type de données [VARIANT](https://docs.snowflake.com/fr/sql-reference/data-types-semistructured).\r\n\r\n\u003E Pour les données qui sont le plus souvent régulières et n’utilisent que des types de données natifs pour le format semi-structuré que vous utilisez (chaînes et entiers pour le format JSON), les exigences de stockage et les performances de requête pour les opérations sur les données relationnelles et les données dans une colonne VARIANT sont très similaires.\r\n\u003E\r\n\r\n### Étape 3 – Parcourir des données semi-structurées à l’aide de la notation par points\r\nDans notre colonne `menu_item_health_metrics_obj`, nous avons vu que `menu_item_id` était inclus avec les données relatives aux ingrédients et aux restrictions alimentaires les plus imbriquées auxquelles nous devons accéder. \r\n\r\nExécutez la requête suivante, qui nous permet de commencer à utiliser la [notation par points](https://docs.snowflake.com/fr/user-guide/querying-semistructured#dot-notation) pour parcourir nos données semi-structurées.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\r\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.3.dot.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.3.dot.png)\r\n\r\nGrâce à la notation par points, nous avons pu extraire complètement `menu_item_id`, mais il semble qu’il reste encore des objets semi-structurés supplémentaires dans la sortie de la colonne `menu_item_health_metrics`. \r\n\r\nCliquons de nouveau sur l’une des cellules de cette colonne pour en examiner le contenu plus en détail.\r\n\r\n![assets/3.3.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/3.3.2.stats.png)\r\n\r\n**Nous progressons !** Dans la prochaine section, voyons comment nous pouvons aller plus loin dans le traitement de `menu_item_health_metrics` en utilisant d’autres fonctions Snowflake.\r\n\r\n### Étape 4 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Aplatir des données semi-structurées\r\n\r\n### Présentation\r\nAprès avoir vu comment nous pouvons facilement interroger des données semi-structurées qui figurent dans une colonne VARIANT à l’aide de la notation par points, en tant que data engineer chez Tasty Bytes, vous êtes bien parti pour fournir aux parties prenantes internes les données qu’elles vous ont demandées.\r\n\r\nDans cette section, nous allons procéder à un traitement plus poussé des données semi-structurées afin de répondre à certains besoins.\r\n\r\n### Étape 1 – Découvrir la fonction d’aplatissement avec vue latérale\r\nCette fonction permet d’extraire de notre colonne `menu_item_health_metrics_obj` davantage de données demandées par nos utilisateurs en aval. Exécutez la requête suivante, qui utilise la fonctionnalité de notation par points dont nous venons de parler, avec les fonctions d’aplatissement ([FLATTEN](https://docs.snowflake.com/fr/sql-reference/functions/flatten)) et de jointure latérale ([LATERAL JOIN](https://docs.snowflake.com/fr/sql-reference/constructs/join-lateral)) de Snowflake pour nous fournir le premier ARRAY `ingredient` qu’on nous a demandé.\r\n\r\n\u003E **FLATTEN :** fonction de table qui prend une colonne VARIANT, OBJECT ou ARRAY et produit une vue latérale. La fonction FLATTEN peut être utilisée pour convertir des données semi-structurées en une représentation relationnelle.\r\n\u003E\r\n\u003E**LATERAL JOIN :** contrairement à la sortie d’une jointure non latérale, la sortie d’une jointure latérale ne comprend que les lignes générées à partir de la vue en ligne. Les lignes du côté gauche n’ont pas besoin d’être reliées au côté droit, car les lignes du côté gauche ont déjà été prises en compte en étant passées dans la vue en ligne. \r\n\u003E\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.1.lat_flat.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.1.lat_flat.png)\r\n\r\n### Étape 2 – Explorer une fonction de tableau (ARRAY)\r\nAvant que nous procédions à l’extraction des données requises relatives aux restrictions alimentaires, exécutez la requête suivante, à l’aide de la fonction de tableau Snowflake [ARRAY_CONTAINS](https://docs.snowflake.com/fr/sql-reference/functions/array_contains), qui vous permettra d’explorer la colonne `ingredients` pour tout `menu_item_name` qui contient de la laitue.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\r\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:\"ingredients\"::VARIANT);\r\n```\r\n\r\n![assets/4.2.array_contains.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.2.array_contains.png)\r\n\r\nLes résultats renvoyés nous montrent que plusieurs de nos éléments de menu incluent de la laitue. Ce type d’analyse serait très utile aux Procurement Managers de notre chaîne d’approvisionnement en cas de rappels d’aliments dans les villes et pays dans lesquels nous intervenons.\r\n\r\n### Étape 3 – Structurer des données semi-structurées à grande échelle\r\nAprès avoir vu le type de valeur ajoutée que nous pouvons déjà apporter à notre organisation, nous pouvons maintenant exécuter la dernière requête de cette section. \r\n\r\nCette requête utilisera la notation par points, ainsi que notre double fonction de table LATERAL JOIN et FLATTEN, pour fournir le jeu de résultats que l’on nous a initialement demandé de générer.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.3.lat_flat_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/4.3.lat_flat_2.png)\r\n\r\n**Parfait !** Ces résultats semblent être conformes aux besoins exacts de nos parties prenantes. Dans la section suivante, nous allons voir comment nous pouvons mettre à profit ces résultats dans notre couche d’analyse, où ils peuvent être consultés par nos parties prenantes.\r\n\r\n### Étape 4 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Créer des vues structurées à partir de données semi-structurées\r\n\r\n### Présentation\r\nDans la section précédente, nous avons créé une requête qui fournit les résultats exacts requis par nos utilisateurs finaux à l’aide d’une suite de fonctionnalités de données semi-structurées Snowflake. Nous allons poursuivre le processus qui consiste à mettre à profit cette requête qui se trouve dans notre couche brute vers la couche harmonisée, puis dans la couche d’analyse, d’où nos utilisateurs finaux sont autorisés à lire des données.\r\n\r\n**Remarque :** pour les habitués des modèles de données Bronze, Silver et Gold, ces derniers correspondent, respectivement, aux couches brute, harmonisée et d’analyse.\r\n\r\n### Étape 1 – Créer une vue harmonisée à l’aide de la requête SQL d’aplatissement des données semi-structurées\r\nEn reprenant la requête exacte de la fin de la section précédente, exécutez la requête suivante, qui contient cette requête SQL ainsi que toutes les autres colonnes de la table `menu` déjà structurées. \r\n\r\nDans cette requête, nous utilisons la commande [CREATE VIEW](https://docs.snowflake.com/fr/sql-reference/sql/create-view) dans notre schéma harmonisé pour encapsuler la logique de traitement des données semi-structurées et d’autres colonnes sous forme de table.\r\n\r\n\u003E Une vue permet de consulter le résultat d’une requête comme s’il s’agissait d’une table. Les vues servent à diverses fins, notamment à combiner, séparer ou protéger des données. \r\n\u003E\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\r\n    AS\r\nSELECT \r\n    m.menu_id,\r\n    m.menu_type_id,\r\n    m.menu_type,\r\n    m.truck_brand_name,\r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    m.item_category,\r\n    m.item_subcategory,\r\n    m.cost_of_goods_usd,\r\n    m.sale_price_usd,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/5.1.harmonized_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/5.1.harmonized_view.png)\r\n\r\nLa vue `harmonized.menu_v` étant créée, nous pourrions désormais l’interroger directement sans avoir à exploiter la requête SQL plus complexe que nous avons utilisée pour la créer. Toutefois, étant donné que la couche d’analyse est celle où nos parties prenantes consultent des données, passons à l’étape suivante pour insérer la requête dans cette couche.\r\n\r\n### Étape 2 – Utiliser la requête de la couche harmonisée dans la couche d’analyse en toute simplicité\r\nÀ partir de la vue `harmonized.menu_v`, exécutez la requête suivante pour créer notre vue `analytics.menu_v`, qui commencera immédiatement à créer de la valeur en aval. \r\n\r\nDans cette requête, nous voyons de nouvelles fonctions dont nous n’avons pas encore parlé. Nous ajoutons tout d’abord une fonction [COMMENT](https://docs.snowflake.com/fr/sql-reference/sql/comment), visible dans les commandes de [SHOW VIEWS](https://docs.snowflake.com/fr/sql-reference/sql/show) ou dans l’interface Snowsight, pour documenter ce qu’un utilisateur peut voir lorsqu’il interroge cette vue.\r\n\r\nNous utilisons également les paramètres [SELECT * EXCLUDE et RENAME](https://docs.snowflake.com/fr/sql-reference/sql/select#parameters), qui peuvent faciliter la vie des développeurs SQL en simplifiant la définition de la requête ou de la vue.\r\n\r\n\u003E **EXCLUDE :** lorsque vous sélectionnez toutes les colonnes (SELECT * ou SELECT table_name.*), EXCLUDE spécifie les colonnes à exclure des résultats.\r\n\u003E\r\n\u003E **RENAME :** lorsque vous sélectionnez toutes les colonnes (SELECT * ou SELECT table_name.*), RENAME spécifie les alias de colonnes qui doivent être utilisés dans les résultats.\r\n\u003E\r\n\r\n![assets/5.2.analytics_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/5.2.analytics_view.png)\r\n\r\n### Étape 3 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Analyser des données semi-structurées traitées dans Snowsight\r\n\r\n### Présentation\r\nNotre vue de menu étant disponible dans notre couche d’analyse, interrogeons-la en exécutant plusieurs requêtes qui montreront à nos utilisateurs finaux comment Snowflake crée une expérience de requête relationnelle à partir de données semi-structurées sans avoir à en faire des copies supplémentaires ou à les traiter de manière complexe.\r\n\r\n### Étape 1 – Analyser des tableaux (ARRAY)\r\nLa requête de la section précédente a renvoyé la colonne `ingredients`, mais celle-ci est désormais disponible sans aucune exigence en matière de notation par points dans notre vue `analytics.menu_v`. \r\n\r\nAvec cette vue, vous devez désormais exécuter la requête suivante, qui exploite deux autres fonctions de tableau Snowflake, [ARRAY_INTERSECTION](https://docs.snowflake.com/fr/sql-reference/functions/array_intersection) et [ARRAYS_OVERLAP](https://docs.snowflake.com/fr/sql-reference/functions/arrays_overlap), pour voir quels éléments de menu hors boissons se chevauchent pour chacun des menus de nos marques de food trucks.\r\n\r\n```\r\nSELECT \r\n    m1.menu_type,\r\n    m1.menu_item_name,\r\n    m2.menu_type AS overlap_menu_type,\r\n    m2.menu_item_name AS overlap_menu_item_name,\r\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\r\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\r\n    ON m1.menu_item_id \u003C\u003E m2.menu_item_id -- avoid joining the same menu item to itself\r\n    AND m1.menu_type \u003C\u003E m2.menu_type \r\nWHERE 1=1\r\n    AND m1.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND m2.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\r\nORDER BY m1.menu_type;\r\n```\r\n\r\n![assets/6.1.arrays.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.1.arrays.png)\r\n\r\nDe nouveau, en utilisant exactement la vue que nous avons créée, ce type de requête pourrait apporter une véritable valeur commerciale aux opérateurs de nos food trucks, en les aidant à prévoir et commander les ingrédients dont ils ont besoin chaque semaine.\r\n\r\n### Étape 2 – Fournir des indicateurs aux dirigeants\r\nEssayons maintenant d’aider les dirigeants de Tasty Bytes à prendre des décisions au sujet des menus basées sur les données, en leur fournissant des indicateurs globaux relatifs aux restrictions alimentaires que nous gérons actuellement dans l’ensemble de nos marques.\r\n\r\nExécutez la requête suivante, qui utilise les fonctions [COUNT](https://docs.snowflake.com/fr/sql-reference/functions/count), [SUM](https://docs.snowflake.com/fr/sql-reference/functions/sum) et [CASE](https://docs.snowflake.com/fr/sql-reference/functions/case) (conditionnelle) pour agréger les indicateurs requis depuis notre vue `analytics.menu_v`.\r\n\r\n```\r\nSELECT\r\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\r\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\r\n```\r\n\r\n![assets/6.2.metrics.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.2.metrics.png)\r\n\r\nLes résultats que nous venons de recevoir prouvent que nous sommes parvenus à passer d’une table brute contenant des données semi-structurées à une seule ligne agrégée, à laquelle tous les membres de notre organisation peuvent accéder afin d’aider Tasty Bytes à adopter un modèle davantage axé sur les données. \r\n\r\n\r\n### Étape 3 – Convertir des résultats en graphiques\r\nCertains des dirigeants de Tasty Bytes préfèrent des représentations visuelles des données. Voyons à quel point il est facile de convertir des résultats renvoyés sous forme de tableau en graphiques visuels faciles à assimiler dans Snowsight. \r\n\r\nAvec la requête SQL que nous avons précédemment utilisée, exécutez la requête suivante, qui ajoute des filtres sur trois noms de nos marques de camionnettes.\r\n\r\n```\r\nSELECT\r\n    m.brand_name,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m\r\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\r\nGROUP BY m.brand_name;\r\n```\r\n\r\n![assets/6.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.3.results.png)\r\n\r\nPar défaut, Snowsight renvoie les résultats des requêtes sous forme de tableau. Cependant, Snowsight contient une fonctionnalité efficace dont nous n’avons pas encore parlé, l’[utilisation des graphiques](https://docs.snowflake.com/fr/user-guide/ui-snowsight-visualizations#using-charts).\r\n\r\nSuivez les flèches figurant sur la capture d’écran ci-dessous pour créer un graphique à barres qui compare les éléments de menu de ces différentes marques de food truck concernant certaines restrictions alimentaires.\r\n\r\n![assets/6.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-fr/6.3.chart.png)\r\n\r\nPour terminer ce guide Quickstart, nous devons préciser à quel point il serait facile pour un dirigeant de Tasty Bytes de réaliser lui-même ce type d’analyse sans avoir à connaître la logique de traitement des données semi-structurées que nous avons encapsulée dans les vues que nous avons créées. Nous sommes ainsi sûrs de participer au développement de la démocratisation des données au sein de l’organisation Tasty Bytes.\r\n\r\n### Étape 4 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Conclusion et étapes suivantes\r\n\r\n### Conclusion\r\nBeau travail ! Vous avez terminé le guide Quickstart Tasty Bytes Débuter avec Snowflake sur les données semi-structurées. \r\n\r\nVous avez : \r\n- Découvert ce qu’est le type de données VARIANT \r\n- Utilisé la notation par points pour interroger des données semi-structurées \r\n- Aplati des données semi-structurées \r\n- Analysé des tableaux (ARRAY) \r\n- Créé deux vues \r\n- Créé un graphique à partir d’un jeu de résultats dans Snowsight\r\n\r\nSi vous voulez recommencer ce guide Quickstart, utilisez les scripts de réinitialisation en bas de votre feuille de calcul associée.\r\n\r\n### Prochaines étapes\r\nPour continuer à découvrir le Data Cloud Snowflake, cliquez sur le lien ci-dessous pour voir tous les autres guides Quickstart Powered by Tasty Bytes à votre disposition.\r\n\r\n- ### [Table des matières des guides Quickstart Powered by Tasty Bytes](/fr/developers/guides/tasty-bytes-introduction-fr/)","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-637fabd6ff","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-57d1bf869b","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3c926f74b0","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-10-31",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-f484cc2af9","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"id":"container-b46a67e4e7","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"id":"container-c4b01266ea","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"id":"container-61eeb6c52f","layout":"SIMPLE","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-8d0e93ef58","fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/tasty-bytes-zero-to-snowflake-semi-structured-data-fr",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003ETraitement de données semi-structurées dans Snowflake\u003C/h2\u003E","\u003Ch2\u003ECréer une feuille de calcul et la copier dans un fichier SQL\u003C/h2\u003E","\u003Ch2\u003EÉtablir le profil des données de menu semi-structurées\u003C/h2\u003E","\u003Ch2\u003EAplatir des données semi-structurées\u003C/h2\u003E","\u003Ch2\u003ECréer des vues structurées à partir de données semi-structurées\u003C/h2\u003E","\u003Ch2\u003EAnalyser des données semi-structurées traitées dans Snowsight\u003C/h2\u003E","\u003Ch2\u003EConclusion et étapes suivantes\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-fafd4b6b89","fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/tasty-bytes-zero-to-snowflake-semi-structured-data-fr",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-99a3b1109a","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:1fr 0}.qs-disclaimer-text p \u003E span{font-size:15px !important}@media (min-width:768px){#quickstart-template-main-flexible-container{padding:24px 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