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Kranzler","quickstartHeroForkRepoLink":{"id":"button-fdd010ae50","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/tasty-bytes-zero-to-snowflake-collaboration-fr"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Tasty Bytes – Débuter avec Snowflake – Collaboration","url":"https://www.snowflake.com/content/snowflake-site/global/fr/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr","currentPage":true},{"title":"Guides pour les développeurs","url":"https://www.snowflake.com/content/snowflake-site/global/fr/developers/guides","currentPage":false},{"title":"Snowflake pour les 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-->\n","\u003Ch2\u003EAccéder directement à des données tierces via la Marketplace Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EBienvenue dans ce guide Quickstart Tasty Bytes Débuter avec Snowflake sur la collaboration ! Dans ce guide, nous allons vous montrer comment vous pouvez enrichir vos données internes avec des données météorologiques tierces via la Marketplace Snowflake. La Marketplace Snowflake vous permet de trouver, d’essayer et d’acheter des applications et données tierces, sans avoir à configurer d’ETL, de pipelines de données ou d’intégrations.\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\u003EComment accéder à la Marketplace Snowflake\u003C/li\u003E\u003Cli\u003EComment acquérir des données Weather Source en direct dans votre compte\u003C/li\u003E\u003Cli\u003EComment créer une vue\u003C/li\u003E\u003Cli\u003EComment créer une fonction SQL\u003C/li\u003E\u003Cli\u003EComment exploiter des graphiques Snowsight pour explorer des informations visuelles\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVous allez :\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHarmoniser des données de ventes internes et des données météorologiques tierces\u003C/li\u003E\u003Cli\u003ECréer une fonction pour convertir facilement des degrés Fahrenheit en degrés Celsius\u003C/li\u003E\u003Cli\u003ECréer une fonction pour convertir facilement des pouces en millimètres\u003C/li\u003E\u003Cli\u003EComprendre comment exploiter davantage d’informations via la Marketplace Snowflake\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-collaboration-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-collaboration-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-collaboration-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 – Collaboration ".\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-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 notre fichier SQL Tasty Bytes hébergé sur GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.sql\"\u003Etb_zts_collaboration.sql\u003C/a\u003E</button>\u003C/p\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-collaboration-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\u003EÉtudier les jours sans vente dans nos données internes\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003EAprès analyse des données de cette année par rapport à celles de l’année dernière, les analystes financiers de Tasty Bytes nous ont fait savoir que certains jours, dans plusieurs villes, leurs camionnettes n’enregistraient aucune vente et qu’ils n’y voyaient aucune explication. Ils nous ont donné l’exemple de la ville de Hambourg, en Allemagne, en février 2022.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Interroger les données des points de ventes pour identifier des tendances\u003C/h3\u003E\n","\u003Cp\u003ECommençons par exécuter trois requêtes pour définir le contexte de notre rôle et de notre entrepôt sur \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E et \u003Ccode\u003Etasty_de_wh\u003C/code\u003E. Une fois le contexte défini, nous interrogerons notre vue d’analyse \u003Ccode\u003Eorders_v\u003C/code\u003E pour fournir un jeu de résultats des ventes pour la ville de Hambourg, en Allemagne, en 2022.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer; \nUSE WAREHOUSE tasty_de_wh;\n\nSELECT \n    o.date, \n    SUM(o.price) AS daily_sales \nFROM frostbyte_tasty_bytes.analytics.orders_v o \nWHERE 1=1 \n    AND o.country = 'Germany' \n    AND o.primary_city = 'Hamburg' \n    AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28' \nGROUP BY o.date \nORDER BY o.date ASC; \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-collaboration-fr/3.1.orders_v.png\" alt=\"assets/3.1.orders_v.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelon ce que nous observons ci-dessus, nous sommes d’accord avec les analystes pour dire qu’il manque les chiffres des ventes quotidiennes de certains jours du mois de février, ce qui prouve qu’ils ont bien repéré une anomalie. Voyons si nous pouvons en découvrir les raisons dans la section suivante.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EExploiter des données Weather Source depuis la Marketplace Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003ED’après ce que nous avons vu dans la section précédente, il semble qu’il manque les chiffres des ventes du 16 au 21 février pour la ville de Hambourg, en Allemagne. Il n’y a pas grand-chose de plus dans nos données internes que nous pouvons utiliser pour y voir plus clair, mais il doit bien y avoir une explication.\u003C/p\u003E\n","\u003Cp\u003EEn exploitant la \u003Ca href=\"/fr/data-cloud/marketplace/\"\u003EMarketplace Snowflake\u003C/a\u003E, nous pouvons immédiatement vérifier les conditions météorologiques extrêmes en consultant l’annonce publique gratuite de Weather Source.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Se procurer les données Weather Source LLC: frostbyte sur la Marketplace Snowflake\u003C/h3\u003E\n","\u003Cp\u003ELa Marketplace Snowflake vous permet de trouver, d’essayer et d’acheter les applications et données dont vous avez besoin pour créer des solutions professionnelles innovantes. Dans cette étape, nous allons accéder à l’annonce \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte\"\u003EWeather Source LLC: frostbyte\u003C/a\u003E pour analyser plus en détail le recul de nos ventes à Hambourg.\u003C/p\u003E\n","\u003Cp\u003EDepuis votre compte Snowflake, suivez les étapes et la vidéo ci-dessous pour acquérir les données de cette annonce.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECliquez sur -> Home (Accueil).\u003C/li\u003E\u003Cli\u003ECliquez sur -> Marketplace.\u003C/li\u003E\u003Cli\u003ERecherchez -> frostbyte.\u003C/li\u003E\u003Cli\u003ECliquez sur -> Weather Source LLC: frostbyte.\u003C/li\u003E\u003Cli\u003ECliquez sur -> Get (Obtenir).\u003C/li\u003E\u003Cli\u003ERenommez la base de données -> FROSTBYTE_WEATHERSOURCE (en lettres majuscules).\u003C/li\u003E\u003Cli\u003EAttribuez l’accès PUBLIC aux rôles supplémentaires.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.1.acquire_weather_source.gif\" alt=\"assets/4.1.acquire_weather_source.gif\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive Weather Source est l’un des principaux fournisseurs de données météorologiques et climatiques mondiales. Sa suite de produits OnPoint fournit aux entreprises les données météorologiques et climatiques dont elles ont besoin pour générer rapidement des informations utiles et exploitables pour un large éventail de cas d’usage dans de nombreux secteurs d’activité.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EÉtape 2 – Harmoniser des données internes et tierces\u003C/h3\u003E\n","\u003Cp\u003EUne fois la base de données partagée \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E en place, exécutez la requête suivante pour créer une vue \u003Ccode\u003Eharmonized.daily_weather_v\u003C/code\u003E associant les deux tables Weather Source et notre table sur les pays et les villes dans lesquels opèrent les food trucks de Tasty Bytes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\n    AS\nSELECT \n    hd.*,\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\n    pc.city_name AS city,\n    c.country AS country_desc\nFROM frostbyte_weathersource.onpoint_id.history_day hd\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\n    ON pc.postal_code = hd.postal_code\n    AND pc.country = hd.country\nJOIN frostbyte_tasty_bytes.raw_pos.country c\n    ON c.iso_country = hd.country\n    AND c.city = hd.city_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-collaboration-fr/4.2.daily_weather_v.png\" alt=\"assets/4.2.daily_weather_v.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EComme nous le voyons dans la définition de la vue ci-dessus, nous associons deux des tables \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E dans le schéma \u003Ccode\u003Eonpoint_id\u003C/code\u003E, puis nous les harmonisons avec la table \u003Ccode\u003Ecountry\u003C/code\u003E de notre base de données \u003Ccode\u003Efrostbyte_tasty_bytes\u003C/code\u003E et notre schéma \u003Ccode\u003Eraw_pos\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENous trouvons généralement ce type d’opération dans la couche harmonisée (également appelée zone Silver).\u003C/p\u003E\n","\u003Ch3\u003EÉtape 3 – Visualiser les températures quotidiennes\u003C/h3\u003E\n","\u003Cp\u003EAvec la vue \u003Ccode\u003Edaily_weather_v\u003C/code\u003E de notre schéma harmonisé en place, jetons un œil à la température quotidienne moyenne à Hambourg en février 2022 en exécutant la requête suivante.\u003C/p\u003E\n","\u003Cp\u003ENous allons aussi exploiter les fonctions \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/avg\"\u003EAVG\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/year\"\u003EYEAR\u003C/a\u003E et \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/year\"\u003EMONTH\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc = 'Germany'\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\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-collaboration-fr/4.3.results.png\" alt=\"assets/4.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPour étudier plus en détail les tendances, utilisons des graphiques Snowsight pour créer un graphique linéaire de la température moyenne au fil du temps.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.3.chart.png\" alt=\"assets/4.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelon ce que nous avons vu avant, il n’existe aucune raison évidente qui explique les jours sans vente de nos camionnettes. Voyons quelle raison nous pouvons trouver pour expliquer cette situation dans la section suivante.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 4 – Rassembler les données sur le vent\u003C/h3\u003E\n","\u003Cp\u003EComme nous l’avons vu à l’étape précédente, la température quotidienne moyenne ne semble pas être la raison de nos jours sans vente à Hambourg. Heureusement, Weather Source fournit d’autres indicateurs météorologiques sur lesquels nous pouvons également nous pencher.\u003C/p\u003E\n","\u003Cp\u003EExécutez la requête suivante qui va exploiter notre vue harmonisée pour rassembler des indicateurs relatifs au vent. Cette requête va utiliser la fonction \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/min\"\u003EMAX\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\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-collaboration-fr/4.4.result.png\" alt=\"assets/4.4.result.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe nouveau, il se peut qu’un graphique Snowsight soit plus efficace pour dégager les tendances de ce type de données. Suivez les flèches figurant sur les captures d’écran ci-dessous pour passer de l’onglet Results (Résultats) à l’onglet Charts (Graphiques).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.4.chart.png\" alt=\"assets/4.4.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAh !\u003C/strong\u003E Les jours où nous n’avons enregistré aucune vente, la ville a connu des vents de force ouragan. Cette information peut expliquer pourquoi nos camionnettes n’ont rien vendu ces jours-là. Toutefois, étant donné que nous avons effectué cette analyse dans la vue harmonisée, mettons ces informations à disposition dans la vue d’analyse afin que nos analystes puissent y accéder eux-mêmes.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 5 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003EDémocratiser des informations extraites de données\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003ENous avons déterminé que des vents de force ouragan ont probablement exercé une influence sur les jours sans vente, une tendance dont nos analystes financiers nous ont fait part.\u003C/p\u003E\n","\u003Cp\u003EMettons désormais ce type de recherche à disposition de l’ensemble des membres de notre organisation en déployant une vue d’analyse à laquelle tous les employés de Tasty Bytes pourront accéder.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Créer des fonctions SQL\u003C/h3\u003E\n","\u003Cp\u003EEn tant qu’entreprise internationale, commençons par créer deux fonctions SQL pour convertir les degrés Fahrenheit en degrés Celsius et les pouces en millimètres.\u003C/p\u003E\n","\u003Cp\u003EExécutez les deux requêtes de cette étape l’une après l’autre pour créer nos fonctions \u003Ccode\u003Efahrenheit_to_celsius\u003C/code\u003E et \u003Ccode\u003Einch_to_millimeter\u003C/code\u003E, qui utilisent la commande \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/create-function\"\u003ECREATE FUNCTION\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\nRETURNS NUMBER(35,4)\nAS\n$$\n    (temp_f - 32) * (5/9)\n$$;\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-collaboration-fr/5.1.f_to_c.png\" alt=\"assets/5.1.f_to_c.png\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\nRETURNS NUMBER(35,4)\n    AS\n$$\n    inch * 25.4\n$$;\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-collaboration-fr/5.1.inch_to_mm.png\" alt=\"assets/5.1.inch_to_mm.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive Lorsque vous créez une UDF, vous spécifiez un gestionnaire dont le code est écrit dans l’un des langages pris en charge. Selon le langage du gestionnaire, vous pouvez soit inclure le code source du gestionnaire en ligne avec l’instruction CREATE FUNCTION, soit faire référence à l’emplacement du gestionnaire à partir de CREATE FUNCTION, où le gestionnaire est précompilé ou le code source est sur une zone de préparation.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EÉtape 2 – Créer une fonction SQL pour une vue\u003C/h3\u003E\n","\u003Cp\u003EAvant de déployer notre vue d’analyse, créons la fonction SQL que nous allons utiliser dans celle-ci pour combiner les données relatives aux ventes quotidiennes et les données météorologiques, mais exploitons aussi nos fonctions de conversion SQL.\u003C/p\u003E\n","\u003Cp\u003EExécutez la requête suivante qui filtre la ville de Hambourg, en Allemagne, et qui exploite des fonctions que nous n’avons pas encore vues, à savoir \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/zeroifnull\"\u003EZEROIFNULL\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/round\"\u003EROUND\u003C/a\u003E et \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/functions/to_date\"\u003EDATE\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nWHERE 1=1\n    AND fd.country_desc = 'Germany'\n    AND fd.city = 'Hamburg'\n    AND fd.yyyy_mm = '2022-02'\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\nORDER BY fd.date_valid_std ASC;\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-collaboration-fr/5.2.SQL.png\" alt=\"assets/5.2.SQL.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELes résultats que nous venons de recevoir semblent optimum. Nous pouvons maintenant insérer cette fonction SQL dans une vue lors de la prochaine étape.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 3 – Déployer une vue d’analyse\u003C/h3\u003E\n","\u003Cp\u003EÀ l’aide de la requête précédente, nous allons devoir supprimer les filtres de la clause WHERE, ajouter une fonction \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E et la promouvoir dans notre schéma \u003Ccode\u003Eanalytics\u003C/code\u003E en tant que vue \u003Ccode\u003Edaily_city_metrics_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EPour ce faire, exécutez la dernière requête de cette section.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\n    AS\nSELECT \n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nWHERE 1=1\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\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-collaboration-fr/5.3.view.png\" alt=\"assets/5.3.view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIncroyable ! Nous avons désormais démocratisé ce type d’informations pour l’organisation Tasty Bytes. Rassemblons toutes ces informations dans la section suivante pour valider notre travail.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 4 – Cliquer sur Next (Suivant) -->\u003C/h3\u003E\n","\u003Ch2\u003ETirer des informations des données de ventes et météorologiques de la Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EPrésentation\u003C/h3\u003E\n","\u003Cp\u003ELes données de ventes et météorologiques étant disponibles pour toutes les villes où nos food trucks opèrent, voyons comment nous avons raccourci le délai d’analyse des analystes financiers.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 1 – Simplifier l’analyse\u003C/h3\u003E\n","\u003Cp\u003EAuparavant, nous devions associer manuellement les données des points de vente et celles de Weather Source pour examiner les problèmes de vente de la ville de Hambourg. Toutefois, nous avons simplifié ce processus via notre vue \u003Ccode\u003Eanalytics.daily_city_metrics_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EExécutez la requête suivante, qui montre à quel point nous avons simplifié cette analyse en faisant d’elle une simple instruction Select depuis une seule et même vue.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dcm.date,\n    dcm.city_name,\n    dcm.country_desc,\n    dcm.daily_sales,\n    dcm.avg_temperature_fahrenheit,\n    dcm.avg_temperature_celsius,\n    dcm.avg_precipitation_inches,\n    dcm.avg_precipitation_millimeters,\n    dcm.max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\nWHERE 1=1\n    AND dcm.country_desc = 'Germany'\n    AND dcm.city_name = 'Hamburg'\n    AND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\nORDER BY date DESC;\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-collaboration-fr/6.1.results.png\" alt=\"assets/6.1.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOui !\u003C/strong\u003E Si ces données avaient été disponibles lors des recherches initiales des analystes financiers, ces derniers n’auraient pas eu besoin de faire appel aux équipes dédiées aux données puisque les informations auraient été à leur disposition.\u003C/p\u003E\n","\u003Cp\u003ETout au long de ce guide Quickstart, nous avons vu comment notre travail nous permet d’optimiser réellement nos performances commerciales et à quel point il est facile d’utiliser la Marketplace Snowflake pour tirer davantage d’informations grâce aux données.\u003C/p\u003E\n","\u003Ch3\u003EÉtape 2 – 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 la collaboration.\u003C/p\u003E\n","\u003Cp\u003EVous avez :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccédé à la Marketplace Snowflake\u003C/li\u003E\u003Cli\u003EAcquis des données Weather Source en direct dans votre compte\u003C/li\u003E\u003Cli\u003ECréé une vue\u003C/li\u003E\u003Cli\u003ECréé une fonction SQL\u003C/li\u003E\u003Cli\u003EUtilisé des graphiques Snowsight pour étudier des informations visuelles\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 – Collaboration","elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Accéder directement à des données tierces via la Marketplace Snowflake\r\n\r\n### Présentation\r\nBienvenue dans ce guide Quickstart Tasty Bytes Débuter avec Snowflake sur la collaboration ! Dans ce guide, nous allons vous montrer comment vous pouvez enrichir vos données internes avec des données météorologiques tierces via la Marketplace Snowflake. La Marketplace Snowflake vous permet de trouver, d’essayer et d’acheter des applications et données tierces, sans avoir à configurer d’ETL, de pipelines de données ou d’intégrations.\r\n\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- Comment accéder à la Marketplace Snowflake\r\n- Comment acquérir des données Weather Source en direct dans votre compte\r\n- Comment créer une vue\r\n- Comment créer une fonction SQL\r\n- Comment exploiter des graphiques Snowsight pour explorer des informations visuelles\r\n\r\n### Vous allez :\r\n- Harmoniser des données de ventes internes et des données météorologiques tierces\r\n- Créer une fonction pour convertir facilement des degrés Fahrenheit en degrés Celsius\r\n- Créer une fonction pour convertir facilement des pouces en millimètres\r\n- Comprendre comment exploiter davantage d’informations via la Marketplace Snowflake\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-collaboration-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-collaboration-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-collaboration-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 – Collaboration \".\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-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 notre fichier SQL Tasty Bytes hébergé sur GitHub.\r\n\r\n\u003Cbutton\u003E[tb_zts_collaboration.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.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-collaboration-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## Étudier les jours sans vente dans nos données internes\r\n\r\n### Présentation\r\nAprès analyse des données de cette année par rapport à celles de l’année dernière, les analystes financiers de Tasty Bytes nous ont fait savoir que certains jours, dans plusieurs villes, leurs camionnettes n’enregistraient aucune vente et qu’ils n’y voyaient aucune explication. Ils nous ont donné l’exemple de la ville de Hambourg, en Allemagne, en février 2022.\r\n\r\n\r\n### Étape 1 – Interroger les données des points de ventes pour identifier des tendances\r\nCommençons par exécuter trois requêtes pour définir le contexte de notre rôle et de notre entrepôt sur `tasty_data_engineer` et `tasty_de_wh`. Une fois le contexte défini, nous interrogerons notre vue d’analyse `orders_v` pour fournir un jeu de résultats des ventes pour la ville de Hambourg, en Allemagne, en 2022.\r\n\r\n``` \r\nUSE ROLE tasty_data_engineer; \r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT \r\n    o.date, \r\n    SUM(o.price) AS daily_sales \r\nFROM frostbyte_tasty_bytes.analytics.orders_v o \r\nWHERE 1=1 \r\n    AND o.country = 'Germany' \r\n    AND o.primary_city = 'Hamburg' \r\n    AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28' \r\nGROUP BY o.date \r\nORDER BY o.date ASC; \r\n```\r\n\r\n![assets/3.1.orders_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/3.1.orders_v.png)\r\n\r\nSelon ce que nous observons ci-dessus, nous sommes d’accord avec les analystes pour dire qu’il manque les chiffres des ventes quotidiennes de certains jours du mois de février, ce qui prouve qu’ils ont bien repéré une anomalie. Voyons si nous pouvons en découvrir les raisons dans la section suivante.\r\n\r\n### Étape 2 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Exploiter des données Weather Source depuis la Marketplace Snowflake\r\n\r\n### Présentation\r\nD’après ce que nous avons vu dans la section précédente, il semble qu’il manque les chiffres des ventes du 16 au 21 février pour la ville de Hambourg, en Allemagne. Il n’y a pas grand-chose de plus dans nos données internes que nous pouvons utiliser pour y voir plus clair, mais il doit bien y avoir une explication. \r\n        \r\nEn exploitant la [Marketplace Snowflake](/fr/data-cloud/marketplace/), nous pouvons immédiatement vérifier les conditions météorologiques extrêmes en consultant l’annonce publique gratuite de Weather Source.\r\n\r\n### Étape 1 – Se procurer les données Weather Source LLC: frostbyte sur la Marketplace Snowflake\r\nLa Marketplace Snowflake vous permet de trouver, d’essayer et d’acheter les applications et données dont vous avez besoin pour créer des solutions professionnelles innovantes. Dans cette étape, nous allons accéder à l’annonce [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) pour analyser plus en détail le recul de nos ventes à Hambourg.\r\n\r\nDepuis votre compte Snowflake, suivez les étapes et la vidéo ci-dessous pour acquérir les données de cette annonce.\r\n\r\n- Cliquez sur -\u003E Home (Accueil).\r\n- Cliquez sur -\u003E Marketplace.\r\n- Recherchez -\u003E frostbyte.\r\n- Cliquez sur -\u003E Weather Source LLC: frostbyte.\r\n- Cliquez sur -\u003E Get (Obtenir).\r\n- Renommez la base de données -\u003E FROSTBYTE_WEATHERSOURCE (en lettres majuscules).\r\n- Attribuez l’accès PUBLIC aux rôles supplémentaires.\r\n\r\n![assets/4.1.acquire_weather_source.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.1.acquire_weather_source.gif)\r\n\r\n\u003Easide positive Weather Source est l’un des principaux fournisseurs de données météorologiques et climatiques mondiales. Sa suite de produits OnPoint fournit aux entreprises les données météorologiques et climatiques dont elles ont besoin pour générer rapidement des informations utiles et exploitables pour un large éventail de cas d’usage dans de nombreux secteurs d’activité.\r\n\u003E\r\n\r\n### Étape 2 – Harmoniser des données internes et tierces\r\nUne fois la base de données partagée `frostbyte_weathersource` en place, exécutez la requête suivante pour créer une vue `harmonized.daily_weather_v` associant les deux tables Weather Source et notre table sur les pays et les villes dans lesquels opèrent les food trucks de Tasty Bytes.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\r\n    AS\r\nSELECT \r\n    hd.*,\r\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\r\n    pc.city_name AS city,\r\n    c.country AS country_desc\r\nFROM frostbyte_weathersource.onpoint_id.history_day hd\r\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\r\n    ON pc.postal_code = hd.postal_code\r\n    AND pc.country = hd.country\r\nJOIN frostbyte_tasty_bytes.raw_pos.country c\r\n    ON c.iso_country = hd.country\r\n    AND c.city = hd.city_name;\r\n```\r\n\r\n![assets/4.2.daily_weather_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.2.daily_weather_v.png)\r\n\r\nComme nous le voyons dans la définition de la vue ci-dessus, nous associons deux des tables `frostbyte_weathersource` dans le schéma `onpoint_id`, puis nous les harmonisons avec la table `country` de notre base de données `frostbyte_tasty_bytes` et notre schéma `raw_pos`. \r\n\r\nNous trouvons généralement ce type d’opération dans la couche harmonisée (également appelée zone Silver).\r\n\r\n### Étape 3 – Visualiser les températures quotidiennes\r\nAvec la vue `daily_weather_v` de notre schéma harmonisé en place, jetons un œil à la température quotidienne moyenne à Hambourg en février 2022 en exécutant la requête suivante.\r\n\r\nNous allons aussi exploiter les fonctions [AVG](https://docs.snowflake.com/fr/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/fr/sql-reference/functions/year) et [MONTH](https://docs.snowflake.com/fr/sql-reference/functions/year).\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc = 'Germany'\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.3.results.png)\r\n\r\nPour étudier plus en détail les tendances, utilisons des graphiques Snowsight pour créer un graphique linéaire de la température moyenne au fil du temps.\r\n\r\n![assets/4.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.3.chart.png)\r\n\r\nSelon ce que nous avons vu avant, il n’existe aucune raison évidente qui explique les jours sans vente de nos camionnettes. Voyons quelle raison nous pouvons trouver pour expliquer cette situation dans la section suivante.\r\n\r\n### Étape 4 – Rassembler les données sur le vent\r\nComme nous l’avons vu à l’étape précédente, la température quotidienne moyenne ne semble pas être la raison de nos jours sans vente à Hambourg. Heureusement, Weather Source fournit d’autres indicateurs météorologiques sur lesquels nous pouvons également nous pencher. \r\n\r\nExécutez la requête suivante qui va exploiter notre vue harmonisée pour rassembler des indicateurs relatifs au vent. Cette requête va utiliser la fonction [MAX](https://docs.snowflake.com/fr/sql-reference/functions/min).\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc IN ('Germany')\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.4.result.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.4.result.png)\r\n\r\nDe nouveau, il se peut qu’un graphique Snowsight soit plus efficace pour dégager les tendances de ce type de données. Suivez les flèches figurant sur les captures d’écran ci-dessous pour passer de l’onglet Results (Résultats) à l’onglet Charts (Graphiques).\r\n\r\n![assets/4.4.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/4.4.chart.png)\r\n\r\n**Ah !** Les jours où nous n’avons enregistré aucune vente, la ville a connu des vents de force ouragan. Cette information peut expliquer pourquoi nos camionnettes n’ont rien vendu ces jours-là. Toutefois, étant donné que nous avons effectué cette analyse dans la vue harmonisée, mettons ces informations à disposition dans la vue d’analyse afin que nos analystes puissent y accéder eux-mêmes.\r\n\r\n### Étape 5 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Démocratiser des informations extraites de données\r\n\r\n### Présentation\r\nNous avons déterminé que des vents de force ouragan ont probablement exercé une influence sur les jours sans vente, une tendance dont nos analystes financiers nous ont fait part.\r\n\r\nMettons désormais ce type de recherche à disposition de l’ensemble des membres de notre organisation en déployant une vue d’analyse à laquelle tous les employés de Tasty Bytes pourront accéder.\r\n\r\n### Étape 1 – Créer des fonctions SQL\r\nEn tant qu’entreprise internationale, commençons par créer deux fonctions SQL pour convertir les degrés Fahrenheit en degrés Celsius et les pouces en millimètres. \r\n\r\nExécutez les deux requêtes de cette étape l’une après l’autre pour créer nos fonctions `fahrenheit_to_celsius` et `inch_to_millimeter`, qui utilisent la commande [CREATE FUNCTION](https://docs.snowflake.com/fr/sql-reference/sql/create-function).\r\n\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\nAS\r\n$$\r\n    (temp_f - 32) * (5/9)\r\n$$;\r\n```\r\n\r\n![assets/5.1.f_to_c.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/5.1.f_to_c.png)\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\n    AS\r\n$$\r\n    inch * 25.4\r\n$$;\r\n```\r\n\r\n![assets/5.1.inch_to_mm.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/5.1.inch_to_mm.png)\r\n\r\n\u003Easide positive Lorsque vous créez une UDF, vous spécifiez un gestionnaire dont le code est écrit dans l’un des langages pris en charge. Selon le langage du gestionnaire, vous pouvez soit inclure le code source du gestionnaire en ligne avec l’instruction CREATE FUNCTION, soit faire référence à l’emplacement du gestionnaire à partir de CREATE FUNCTION, où le gestionnaire est précompilé ou le code source est sur une zone de préparation.\r\n\u003E\r\n\r\n### Étape 2 – Créer une fonction SQL pour une vue\r\nAvant de déployer notre vue d’analyse, créons la fonction SQL que nous allons utiliser dans celle-ci pour combiner les données relatives aux ventes quotidiennes et les données météorologiques, mais exploitons aussi nos fonctions de conversion SQL. \r\n\r\nExécutez la requête suivante qui filtre la ville de Hambourg, en Allemagne, et qui exploite des fonctions que nous n’avons pas encore vues, à savoir [ZEROIFNULL](https://docs.snowflake.com/fr/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/fr/sql-reference/functions/round) et [DATE](https://docs.snowflake.com/fr/sql-reference/functions/to_date).\r\n\r\n```\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\n    AND fd.country_desc = 'Germany'\r\n    AND fd.city = 'Hamburg'\r\n    AND fd.yyyy_mm = '2022-02'\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\r\nORDER BY fd.date_valid_std ASC;\r\n```\r\n\r\n![assets/5.2.SQL.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/5.2.SQL.png)\r\n\r\n\r\nLes résultats que nous venons de recevoir semblent optimum. Nous pouvons maintenant insérer cette fonction SQL dans une vue lors de la prochaine étape.\r\n\r\n### Étape 3 – Déployer une vue d’analyse\r\nÀ l’aide de la requête précédente, nous allons devoir supprimer les filtres de la clause WHERE, ajouter une fonction [COMMENT](https://docs.snowflake.com/fr/sql-reference/sql/comment) et la promouvoir dans notre schéma `analytics` en tant que vue `daily_city_metrics_v`.\r\n\r\nPour ce faire, exécutez la dernière requête de cette section.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\r\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\r\n    AS\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\r\n```\r\n\r\n![assets/5.3.view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/5.3.view.png)\r\n\r\nIncroyable ! Nous avons désormais démocratisé ce type d’informations pour l’organisation Tasty Bytes. Rassemblons toutes ces informations dans la section suivante pour valider notre travail.\r\n\r\n### Étape 4 – Cliquer sur Next (Suivant) --\u003E\r\n\r\n## Tirer des informations des données de ventes et météorologiques de la Marketplace\r\n\r\n### Présentation\r\nLes données de ventes et météorologiques étant disponibles pour toutes les villes où nos food trucks opèrent, voyons comment nous avons raccourci le délai d’analyse des analystes financiers.\r\n\r\n### Étape 1 – Simplifier l’analyse\r\nAuparavant, nous devions associer manuellement les données des points de vente et celles de Weather Source pour examiner les problèmes de vente de la ville de Hambourg. Toutefois, nous avons simplifié ce processus via notre vue `analytics.daily_city_metrics_v`. \r\n\r\nExécutez la requête suivante, qui montre à quel point nous avons simplifié cette analyse en faisant d’elle une simple instruction Select depuis une seule et même vue.\r\n\r\n```\r\nSELECT \r\n    dcm.date,\r\n    dcm.city_name,\r\n    dcm.country_desc,\r\n    dcm.daily_sales,\r\n    dcm.avg_temperature_fahrenheit,\r\n    dcm.avg_temperature_celsius,\r\n    dcm.avg_precipitation_inches,\r\n    dcm.avg_precipitation_millimeters,\r\n    dcm.max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\r\nWHERE 1=1\r\n    AND dcm.country_desc = 'Germany'\r\n    AND dcm.city_name = 'Hamburg'\r\n    AND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\r\nORDER BY date DESC;\r\n```\r\n\r\n![assets/6.1.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-fr/6.1.results.png)\r\n\r\n**Oui !** Si ces données avaient été disponibles lors des recherches initiales des analystes financiers, ces derniers n’auraient pas eu besoin de faire appel aux équipes dédiées aux données puisque les informations auraient été à leur disposition. \r\n\r\nTout au long de ce guide Quickstart, nous avons vu comment notre travail nous permet d’optimiser réellement nos performances commerciales et à quel point il est facile d’utiliser la Marketplace Snowflake pour tirer davantage d’informations grâce aux données.\r\n\r\n### Étape 2 – 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 la collaboration. \r\n\r\nVous avez : \r\n- Accédé à la Marketplace Snowflake \r\n- Acquis des données Weather Source en direct dans votre compte \r\n- Créé une vue \r\n- Créé une fonction SQL \r\n- Utilisé des graphiques Snowsight pour étudier des informations visuelles\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-42c6cd7167","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-998ecf4125","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-240c14b1fc","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-6a5483add4","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 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directement à des données tierces via la Marketplace Snowflake\u003C/h2\u003E","\u003Ch2\u003ECréer une feuille de calcul et la copier dans un fichier SQL\u003C/h2\u003E","\u003Ch2\u003EÉtudier les jours sans vente dans nos données internes\u003C/h2\u003E","\u003Ch2\u003EExploiter des données Weather Source depuis la Marketplace Snowflake\u003C/h2\u003E","\u003Ch2\u003EDémocratiser des informations extraites de données\u003C/h2\u003E","\u003Ch2\u003ETirer des informations des données de ventes et météorologiques de la Marketplace\u003C/h2\u003E","\u003Ch2\u003EConclusion et étapes 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