\nfrom pyspark.sql.functions import col\n<\/pre><\/div>\n\n\nEnsuite, nous allons d\u00e9finir nos diff\u00e9rents noms de fichiers et chemins de fichiers sur notre lake.<\/p>\n\n\n
\n# zone urls\nbronzeUrl = 'abfss:\/\/<name of your container>@<name of your storage account>.dfs.core.windows.net'\nsilverUrl = 'abfss:\/\/<name of your container>@<name of your storage account>.dfs.core.windows.net'\n\n# source files\nbronzeInvoiceLinesFile = '\/staging\/InvoiceLines\/InvoiceLines.parquet'\nbronzeInvoicesFile = '\/staging\/Invoices\/Invoices.parquet'\n\n# destination file\nsilverInvoicesFile = '\/Invoices'\n<\/pre><\/div>\n\n\n<\/span>La transformation !<\/span><\/h3>\n\n\n\nPour commencer, nous allons avoir besoin de lire nos diff\u00e9rents fichiers sources et de les stocker dans un dataframe. En nous servant du code que synapse nous avait g\u00e9n\u00e9r\u00e9 plus t\u00f4t, nous allons remplacer le chemin de nos fichiers par nos variables.<\/p>\n\n\n
\ndfInvoiceLines = spark.read.load(f'{bronzeUrl}{bronzeInvoiceLinesFile}', format='parquet')\ndfInvoices = spark.read.load(f'{bronzeUrl}{bronzeInvoicesFile}', format='parquet')\n<\/pre><\/div>\n\n\nNous arrivons maintenant \u00e0 notre premier sujet sensible. Nous voulons joindre deux dataframe qui si on les analyse un minimum comportent certaines colonnes qui ont le m\u00eame nom. Si dans certains cas cela ne pose pas de probl\u00e8me, Spark ne nous laissera pas \u00e9crire un fichier parquet avec deux colonnes qui portent le m\u00eame nom. Nous allons avoir besoin de les renommer \u00e0 un moment donn\u00e9.<\/p>\n\n\n\n
D’autre part, m\u00eame si dans le cas pr\u00e9sent nous allons faire une jointure entre deux tables de la m\u00eame base de donn\u00e9e source. Et, que de ce fait le \u00ab\u00a0lineage\u00a0\u00bb est relativement simple, j’ai pour habitude de pr\u00e9fixer les noms de colonnes pour identifier plus facilement la provenance des informations et ainsi ne plus avoir de colonnes avec le m\u00eame nom.<\/p>\n\n\n\n
Nous n’allons \u00e9videmment pas faire ceci \u00e0 la main en \u00e9num\u00e9rant l’int\u00e9gralit\u00e9 des colonnes pour plusieurs raisons :<\/p>\n\n\n\n
\nNous sommes fain\u00e9ants<\/li>\n\n\n\n Les sch\u00e9mas pourraient changer et nous aurions besoin d’aller modifier notre code<\/li>\n\n\n\n Nous sommes fain\u00e9ants !<\/li>\n<\/ul>\n\n\n\nLa solution sera gr\u00e2ce \u00e0 python d’it\u00e9rer sur l’ensemble de nos colonnes (d’o\u00f9 le besoin de la fonction col import\u00e9 plus haut), et d’utiliser la fonction alias pour changer le nom d’une colonne (en ajoutant \u00ab\u00a0line_\u00a0\u00bb \u00e0 toutes les colonnes de la table des lignes de factures) et d’assigner le dataframe ainsi g\u00e9n\u00e9r\u00e9 \u00e0 notre dataframe de d\u00e9part.<\/p>\n\n\n
\ndfInvoiceLines = dfInvoiceLines.select(*(col(x).alias('line_' + x) for x in dfInvoiceLines.columns))\n<\/pre><\/div>\n\n\nC’est maintenant le moment tant attendu de la jointure. Nous allons simplement \u00e9crire un left outer join ou \u00ab\u00a0jointure externe gauche\u00a0\u00bb (un inner join pourrait fonctionner si l’on est certains de l’int\u00e9grit\u00e9 entre lignes et en-t\u00eate).<\/p>\n\n\n
\nJoinCondition = [dfInvoiceLines.line_InvoiceID==dfInvoices.InvoiceID]\ndfSilverInvoices = dfInvoiceLines.join(dfInvoices,JoinCondition,"left_outer")\n<\/pre><\/div>\n\n\n<\/span>Nos donn\u00e9es en zone Silver<\/span><\/h3>\n\n\n\nEt tout ceci ne servirait \u00e0 rien sans l’\u00e9tape finale qui consiste \u00e0 \u00e9crire le fichier r\u00e9sultant dans notre zone de datalake Silver.<\/p>\n\n\n
\ndfSilverInvoices.write.mode("overwrite").parquet(silverUrl + silverInvoicesFile)\n<\/pre><\/div>\n\n\n<\/span>Le Notebook final<\/span><\/h3>\n\n\n\nLe format d’un notebook et ses diff\u00e9rentes cellules n’\u00e9tant pas le moyen le plus simple \u00e0 partager en \u00ab\u00a0code pur\u00a0\u00bb, voici une capture d’\u00e9cran de ce \u00e0 quoi doit ressembler votre notebook si vous avez copier l’ensemble des bouts de codes pr\u00e9c\u00e9dents dans l’ordre. La s\u00e9paration des cellules et l’ajout des annotations en Markdown n’ont aucune importance dans notre exemple. La seule chose qui \u00e0 de l’importance est l’ordre des cellules car notre Notebook sera ex\u00e9cut\u00e9 cellule apr\u00e8s cellule de haut en bas.<\/p>\n\n\n\nNotebook de transformation complet<\/figcaption><\/figure>\n\n\n\n<\/span>Conclusion<\/span><\/h2>\n\n\n\nCe premier Notebook est tr\u00e8s simple et le code Spark \u00e9crit n’est pas forc\u00e9ment robuste aux erreurs ni techniquement optimis\u00e9. Il a cependant le m\u00e9rite de fonctionner pour nous permettre d’avancer dans la cr\u00e9ation de notre datalakehouse.<\/p>\n\n\n\n
A partir de cet exemple, il est possible de dupliquer le code en changeant les noms de fichiers (ainsi que les r\u00e8gles de calculs si besoin) pour g\u00e9rer les autres tables de faits de WWI. Il est possible de traiter les Orders et OrderLines sur le m\u00eame sch\u00e9ma exactement.<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Synapse nous permet d’utiliser Apache Spark en tant que Runtime de processing afin de travailler nos diff\u00e9rents datasets. Dans cet article, nous allons cr\u00e9er notre premier notebook Spark (et le pool Spark n\u00e9cessaire) pour transformer nos premi\u00e8res donn\u00e9es de la zone bronze pour commencer \u00e0 alimenter notre zone silver. Introduction et pool Spark Un Notebook […]<\/p>\n","protected":false},"author":2,"featured_media":712,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[76],"tags":[9,80,79],"class_list":["post-680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-synapsedatalake","tag-azure-synapse-analytics","tag-notebook","tag-spark"],"yoast_head":"\n
Notre premier notebook Spark dans Synapse - NiceData<\/title>\n<meta name=\"description\" content=\"Nous allons cr\u00e9er notre premier notebook Spark (et le pool Spark n\u00e9cessaire) pour transformer nos premi\u00e8res donn\u00e9es de la zone bronze pour commencer \u00e0 alimenter notre zone silver.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Notre premier notebook Spark dans Synapse - NiceData\" \/>\n<meta property=\"og:description\" content=\"Nous allons cr\u00e9er notre premier notebook Spark (et le pool Spark n\u00e9cessaire) pour transformer nos premi\u00e8res donn\u00e9es de la zone bronze pour commencer \u00e0 alimenter notre zone silver.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/\" \/>\n<meta property=\"og:site_name\" content=\"NiceData\" \/>\n<meta property=\"article:published_time\" content=\"2024-08-17T12:09:42+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-10-07T13:07:27+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.nicedata.fr\/wp-content\/uploads\/2024\/09\/notebookDrawing-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Jean-Laurent Ferralis\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@JLFerralis\" \/>\n<meta name=\"twitter:site\" content=\"@JLFerralis\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Jean-Laurent Ferralis\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"14 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/\"},\"author\":{\"name\":\"Jean-Laurent Ferralis\",\"@id\":\"https:\/\/www.nicedata.fr\/#\/schema\/person\/8d1ad38004d3b0cf6bff7c200c795e19\"},\"headline\":\"Notre premier notebook Spark dans Synapse\",\"datePublished\":\"2024-08-17T12:09:42+00:00\",\"dateModified\":\"2024-10-07T13:07:27+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/\"},\"wordCount\":2261,\"commentCount\":1,\"publisher\":{\"@id\":\"https:\/\/www.nicedata.fr\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.nicedata.fr\/index.php\/2024\/08\/17\/notre-premier-notebook-spark-dans-synapse\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/i0.wp.com\/www.nicedata.fr\/wp-content\/uploads\/2024\/09\/notebookDrawing-scaled.jpg?fit=2560%2C1707&ssl=1\",\"keywords\":[\"Azure Synapse Analytics\",\"Notebook\",\"Spark\"],\"articleSection\":[\"Azure Synapse - 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BI consultant and #sql lover. I also #swimbikerun when possible ! Living in @villedenice","sameAs":["http:\/\/xp-it.com"],"url":"https:\/\/www.nicedata.fr\/index.php\/author\/jlf\/"}]}},"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.nicedata.fr\/wp-content\/uploads\/2024\/09\/notebookDrawing-scaled.jpg?fit=2560%2C1707&ssl=1","jetpack-related-posts":[],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/posts\/680","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/comments?post=680"}],"version-history":[{"count":7,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/posts\/680\/revisions"}],"predecessor-version":[{"id":716,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/posts\/680\/revisions\/716"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/media\/712"}],"wp:attachment":[{"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/media?parent=680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/categories?post=680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicedata.fr\/index.php\/wp-json\/wp\/v2\/tags?post=680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}