{"id":59840,"date":"2024-12-03T08:22:37","date_gmt":"2024-12-03T07:22:37","guid":{"rendered":"https:\/\/www.cubeserv.com\/?p=59840"},"modified":"2024-12-03T09:10:02","modified_gmt":"2024-12-03T08:10:02","slug":"datenablage-in-powerbi","status":"publish","type":"post","link":"https:\/\/www.cubeserv.com\/de\/datenablage-in-powerbi\/","title":{"rendered":"Optimale Datenablage f\u00fcr Microsoft Power BI \u2013 Auswertungen"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"59840\" class=\"elementor elementor-59840\" data-elementor-settings=\"{&quot;element_pack_global_tooltip_width&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_padding&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true}}\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8b70e5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8b70e5b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f1b983\" data-id=\"9f1b983\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b89297f elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"b89297f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p class=\"MsoNormal\">In diesem Beitrag wird untersucht, wie strukturierte Daten optimal abgelegt werden sollten, um effizient mit Mircosoft Power BI visualisiert werden zu k\u00f6nnen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-80735d1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"80735d1\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-006650d\" data-id=\"006650d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-eb215fe elementor-widget elementor-widget-heading\" data-id=\"eb215fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h1>Versuchsanlage<\/h1><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-417ab2f elementor-widget elementor-widget-text-editor\" data-id=\"417ab2f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p class=\"MsoNormal\">Die Daten sind in einer lokalen Oracle-Datenbank gespeichert und via einer View abrufbar.<\/p><p class=\"MsoNormal\">Um die Datenmenge in der View zu skalieren, ist der View eine Tabelle hinzugef\u00fcgt, welche bei der Abfrage ein kartesisches Produkt bildet. Damit ist es m\u00f6glich, die Datenmenge auf einfache Weise zu variieren.<\/p><p class=\"MsoNormal\"><span style=\"color: var( --e-global-color-text )\">Es sollen in der Versuchsanlage einmal rund 100\u2018000 Datens\u00e4tze und einmal rund 10 Mio. Datens\u00e4tze ausgewertet werden.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a1b30de elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a1b30de\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-80c3ca2\" data-id=\"80c3ca2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d7c89dd elementor-widget elementor-widget-heading\" data-id=\"d7c89dd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h1>Zugriff auf die Datenquelle<\/h1><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9a8bf22 elementor-widget elementor-widget-text-editor\" data-id=\"9a8bf22\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Untersucht werden folgende Varianten<\/p><ul><li class=\"MsoNormal\"><span lang=\"DE-CH\">V1: Import aus Quellsystem<\/span><\/li><li class=\"MsoNormal\"><span lang=\"DE-CH\">V2: Direct Query auf das Quellsystem<\/span><\/li><li class=\"MsoNormal\"><span lang=\"DE-CH\">V3: Direct Lake auf ein Lakehouse<\/span><\/li><li class=\"MsoNormal\"><span lang=\"DE-CH\">V4: Direkt Lake auf ein Warehouse<\/span><\/li><\/ul><p class=\"MsoNormal\"><span lang=\"DE-CH\">Die Auswertungen zu Varianten V1+V2 werden \u00fcber den Power BI-Client erstellt und dann ver\u00f6ffentlicht.<\/span><\/p><p class=\"MsoNormal\"><span lang=\"DE-CH\">Bei den Varianten V3+V4 wird erst jeweils ein DataFlow f\u00fcr die Aktualisierung der Daten in MS Data Fabric erstellt und darauf die Auswertung mit Power BI erstellt.<\/span><\/p><p class=\"MsoNormal\"><span lang=\"DE-CH\">Bei allen Auswertungsvarianten erstellt Power BI jeweils ein semantisches Model.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6959a2a elementor-widget elementor-widget-image\" data-id=\"6959a2a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"356\" height=\"436\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild1.png\" class=\"attachment-large size-large wp-image-59842\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild1.png 356w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild1-245x300.png 245w\" sizes=\"(max-width: 356px) 100vw, 356px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c0e51e elementor-widget elementor-widget-text-editor\" data-id=\"6c0e51e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Das Modell von V1 h\u00e4lt die Daten im Modell und muss eingeplant werden, um die Daten zu aktualisieren. Bei dieser Aktualisierung kann in Modellen, bei welchen garantiert ist, dass ein bestimmter Teil der Daten sich nicht mehr ver\u00e4ndert, eine inkrementelle Aktualisierung definiert werden, was den Aktualisierungsvorgang beschleunigen kann. Auf eine inkrementelle Aktualisierung wird bei dieser Untersuchung nicht weiter eingegangen, da alle Daten der Tabelle \u00fcber die Zeit ver\u00e4nderbar sind.<\/p><p>Das Modell V2 h\u00e4lt keine Daten im Modell. Bei der Aktualisierung wird lediglich die Struktur und die Definitionen im semantischen Modell aktualisiert. Daher ist der Zeitaufwand f\u00fcr eine Aktualisierung sehr klein.<\/p><p>Die Modelle V3+V4 halten ebenfalls Daten im Modell. Diese semantischen Modelle k\u00f6nnen eingeplant oder bei Aktualisierung des zugrundeliegenden Datentabellen im Lakehouses\/Warehouses automatisch nachgef\u00fchrt werden.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d16f1d6 elementor-widget elementor-widget-image\" data-id=\"d16f1d6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"756\" height=\"261\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild2.png\" class=\"attachment-large size-large wp-image-59845\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild2.png 756w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild2-300x104.png 300w\" sizes=\"(max-width: 756px) 100vw, 756px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79c537d elementor-widget elementor-widget-heading\" data-id=\"79c537d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Zeitbedarf f\u00fcr die Aktualisierung des semantischen Layers bei manueller Aktualisierung<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76715c4 elementor-widget elementor-widget-image\" data-id=\"76715c4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"323\" height=\"158\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild3.png\" class=\"attachment-large size-large wp-image-59848\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild3.png 323w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild3-300x147.png 300w\" sizes=\"(max-width: 323px) 100vw, 323px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08f3fa2 elementor-widget elementor-widget-text-editor\" data-id=\"08f3fa2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bei den Modellen V3+V4 kommt zu diesen Zeiten noch die Beladung des Lakehouse\/Warehouse hinzu. Diese Beladung wird jeweils initial \u00fcber einen Dataflow Gen2 beladen und f\u00fcr die Aktualisierung der Daten jeweils \u00fcber eine Data Pipeline nachgef\u00fchrt.<\/p><p>Bei der initialen Beladung werden die Daten aus der Quelle \u00fcberschreibend in die jeweilige Zieltabelle geschrieben.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f4b397 elementor-widget elementor-widget-image\" data-id=\"0f4b397\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"400\" height=\"312\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild4.png\" class=\"attachment-full size-full wp-image-59851\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild4.png 400w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild4-300x234.png 300w\" sizes=\"(max-width: 400px) 100vw, 400px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eda582b elementor-widget elementor-widget-heading\" data-id=\"eda582b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Dauer der Initial-Beladung <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c1e035 elementor-widget elementor-widget-image\" data-id=\"4c1e035\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"331\" height=\"156\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild5.png\" class=\"attachment-large size-large wp-image-59854\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild5.png 331w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild5-300x141.png 300w\" sizes=\"(max-width: 331px) 100vw, 331px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c1fbde elementor-widget elementor-widget-text-editor\" data-id=\"5c1fbde\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bei der Aktualisierung der Daten werden alle Daten der Quelle, welche einen neueren Zeitstempel als der maximale Zeitstempel im Ziel haben, in eine tempor\u00e4re Tabelle geladen und dann die Daten im Ziel mit denjenigen Daten aus der tempor\u00e4ren Tabelle aktualisiert.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48fe674 elementor-widget elementor-widget-image\" data-id=\"48fe674\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"756\" height=\"338\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild6.png\" class=\"attachment-large size-large wp-image-59857\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild6.png 756w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild6-300x134.png 300w\" sizes=\"(max-width: 756px) 100vw, 756px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3d2b8f6 elementor-widget elementor-widget-text-editor\" data-id=\"3d2b8f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Hierbei werden die hierzu ben\u00f6tigten Notebooks bei der Lakehouse-Beladung mit Spark SQL, bei der Warehouse-Beladung mit T-SQL umgesetzt<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47699a4 elementor-widget elementor-widget-heading\" data-id=\"47699a4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Dauer der Differenz-Beladung<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6fe7b24 elementor-widget elementor-widget-image\" data-id=\"6fe7b24\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"331\" height=\"162\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild7.png\" class=\"attachment-large size-large wp-image-59860\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild7.png 331w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild7-300x147.png 300w\" sizes=\"(max-width: 331px) 100vw, 331px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d65116 elementor-widget elementor-widget-text-editor\" data-id=\"2d65116\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Wir sehen, dass der Overhead bei der Differenz-Beladung betr\u00e4chtlich ist. Wurde seit dem letzten Load keine Daten ver\u00e4ndert, so dauert der Abgleich, notabene von 0 Daten, nur unwesentlich k\u00fcrzer als ein Update von 25000 Datens\u00e4tze bei 10 Mio. Datens\u00e4tzen insgesamt.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-740459f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"740459f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e157d7e\" data-id=\"e157d7e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3a49907 elementor-widget elementor-widget-heading\" data-id=\"3a49907\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h1>Aktualisierung Bericht<\/h1><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f1b7f28 elementor-widget elementor-widget-text-editor\" data-id=\"f1b7f28\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Alle Berichte V1-V4 sind mit den identischen Berichtselementen best\u00fcckt.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2923b2e elementor-widget__width-inherit elementor-widget elementor-widget-image\" data-id=\"2923b2e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild8.png\" title=\"Bild8\" alt=\"Bild8\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d1e5fe elementor-widget elementor-widget-text-editor\" data-id=\"4d1e5fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Es wird jeweils die Summe der Area eines Dimensionswertes in einem Kreisdiagramm ausgegeben. Dabei kann nach einer anderen Dimension \u00fcber den ganzen Bericht gefiltert werden.<\/p><p>Mess-Kriterium hierbei ist der Zeitbedarf, bis der Bericht nach der h\u00f6chsten Filterauspr\u00e4gung (WSJ-790) gefiltert werden kann.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3c30ee8 elementor-widget elementor-widget-image\" data-id=\"3c30ee8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"328\" height=\"157\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild9.png\" class=\"attachment-large size-large wp-image-59866\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild9.png 328w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild9-300x144.png 300w\" sizes=\"(max-width: 328px) 100vw, 328px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6df6757 elementor-widget elementor-widget-text-editor\" data-id=\"6df6757\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p class=\"MsoNormal\"><span lang=\"DE-CH\">Wir sehen, dass bei 100000 Datens\u00e4tzen bei allen Reports das Mess-Kriterium identisch ist. Bei 10 Mio. Datens\u00e4tzen wird aber bei Direkt Query V2 wesentlich mehr Zeit bis zur freien Navigation im Report ben\u00f6tigt.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8ac79a3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8ac79a3\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38d691c\" data-id=\"38d691c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-eb2e594 elementor-widget elementor-widget-heading\" data-id=\"eb2e594\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h1>Gesamt-Ergebnis<\/h1><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7979000 elementor-widget elementor-widget-text-editor\" data-id=\"7979000\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Berechnen wir nun die Gesamt-Zeit, welche ben\u00f6tigt wird, um Daten (bei 250000 zu aktualisierende Datens\u00e4tze) in diesem Bericht zu visualisieren, so kommen wir zu folgender Auflistung:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64c8bed elementor-widget elementor-widget-image\" data-id=\"64c8bed\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"327\" height=\"162\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild10.png\" class=\"attachment-large size-large wp-image-59869\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild10.png 327w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2024\/11\/Bild10-300x149.png 300w\" sizes=\"(max-width: 327px) 100vw, 327px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b90b455 elementor-widget elementor-widget-text-editor\" data-id=\"b90b455\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Wir sehen, dass bei kleinen Datenmengen von 100&#8217;000 Datens\u00e4tzen, Import V1 und Direkt Query V2 das optimale Ergebnis liefern. Sollen jedoch gr\u00f6ssere Datenmengen in der Gr\u00f6\u00dfenordnung von 10 Mio. Datens\u00e4tzen im Report aktualisiert werden, so w\u00fcrde vermeintlich Direkt Query V2 das Rennen machen. Dies stimmt so aber nicht ganz, da, wenn der Endbenutzer den Report \u00f6ffnet und Filtern will, muss dieser hier am l\u00e4ngsten warten. Mit dieser Wartezeit ist bei jedem Aufruf zu rechnen, was bei den Endbenutzern nicht hinzunehmen sein wird.<\/p><p>Daher ist hier auf eine der Varianten V1, V2 oder V3 auszuweichen. Nur diese Varianten werden von den Endbenutzern akzeptiert.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c29e5f4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c29e5f4\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2146923\" data-id=\"2146923\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3afebd4 elementor-widget elementor-widget-heading\" data-id=\"3afebd4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Fazit<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e066f30 elementor-widget elementor-widget-text-editor\" data-id=\"e066f30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bei kleineren zu visualisierenden Datenmengen bietet sich im Umfeld von Power BI sicher die Varianten mit Direkt Query V2 oder Import der Daten V1 an. Sollen jedoch grosse Datenmengen ausgewertet werden, so ist V2 nicht geeignet, h\u00e4ngt aber sonst etwas von den Vorlieben und Erfahrungen des Entwicklers ab.<\/p><p>Einige \u00dcberlegungen hierzu:<\/p><p>Beim Import V1 werden bei jeder Aktualisierung alle Daten aus der semantischen Schicht gel\u00f6scht und neu importiert.<\/p><p>Bei der Variante V3 Lakehouse werden strukturierte Daten in ein Schema gepresst, welches auch f\u00fcr unstrukturierte Daten vorgesehen ist.<\/p><p>Die Variante V4 Warehouse ist nach meiner Einsch\u00e4tzung, auch nach meinen Vorlieben der optimale Ablageort der Daten. Zum einen wird ein System verwendet, welches f\u00fcr die vorliegenden strukturierten Daten vorgesehen ist, zum anderen werden nur Daten im Warehouse manipuliert, welche im Source-System auch ver\u00e4ndert wurden.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-08917f6 elementor-section-height-min-height elementor-section-content-top elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"08917f6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-62c5fb3\" data-id=\"62c5fb3\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-b5a37c7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b5a37c7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-edff6a6\" data-id=\"edff6a6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-80963d0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"80963d0\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-681edd5\" data-id=\"681edd5\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6728ca4 elementor-headline--style-highlight elementor-widget elementor-widget-animated-headline\" data-id=\"6728ca4\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;marker&quot;:&quot;underline&quot;,&quot;highlighted_text&quot;:&quot;Expert Call.&quot;,&quot;headline_style&quot;:&quot;highlight&quot;,&quot;loop&quot;:&quot;yes&quot;,&quot;highlight_animation_duration&quot;:1200,&quot;highlight_iteration_delay&quot;:8000}\" data-widget_type=\"animated-headline.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"elementor-headline\">\n\t\t\t\t\t<span class=\"elementor-headline-plain-text elementor-headline-text-wrapper\">Vereinbaren Sie jetzt Ihren<\/span>\n\t\t\t\t<span class=\"elementor-headline-dynamic-wrapper elementor-headline-text-wrapper\">\n\t\t\t\t\t<span class=\"elementor-headline-dynamic-text elementor-headline-text-active\">Expert Call.<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t<span class=\"elementor-headline-plain-text elementor-headline-text-wrapper\">Wir freuen uns \u00fcber Ihre Nachricht.<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-1498d65 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1498d65\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-d050426\" data-id=\"d050426\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-622b29a elementor-author-box--image-valign-middle elementor-author-box--avatar-yes elementor-author-box--link-no elementor-widget elementor-widget-author-box\" data-id=\"622b29a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div  class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2019\/10\/ackermann_silvio-300x300.jpg\" alt=\"Picture of Silvio Ackermann\" loading=\"lazy\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-15e4b14\" data-id=\"15e4b14\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8212bcd elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"8212bcd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Silvio Ackermann<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0ec723c elementor-widget elementor-widget-heading\" data-id=\"0ec723c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data Warehouse design, ETL creation and Advanced Analytics on Expert level with interests on technologies and challenges<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-f58f98a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f58f98a\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-4d00e99\" data-id=\"4d00e99\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6a6f77a elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"6a6f77a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"tel:+41%2079%20789%2007%2013\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-phone-alt\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M497.39 361.8l-112-48a24 24 0 0 0-28 6.9l-49.6 60.6A370.66 370.66 0 0 1 130.6 204.11l60.6-49.6a23.94 23.94 0 0 0 6.9-28l-48-112A24.16 24.16 0 0 0 122.6.61l-104 24A24 24 0 0 0 0 48c0 256.5 207.9 464 464 464a24 24 0 0 0 23.4-18.6l24-104a24.29 24.29 0 0 0-14.01-27.6z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">+41 79 789 07 13<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/jf.sabjetzki@cubeserv.com\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span 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Damit ist es m\u00f6glich, die Datenmenge auf einfache Weise zu variieren.<br \/>\nEs sollen in der Versuchsanlage einmal rund 100\u2018000 Datens\u00e4tze und einmal rund 10 Mio. Datens\u00e4tze ausgewertet werden.<\/p>\n","protected":false},"author":56,"featured_media":39422,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[508,622,676],"tags":[624,625,626,627,401,647],"class_list":["post-59840","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics","category-microsoft-azure","category-microsoft-fabric","tag-azure","tag-azure-analytics","tag-azure-synapse","tag-data-factory","tag-sap","tag-sap-cdc"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Optimale Datenablage f\u00fcr Microsoft Power BI \u2013 Auswertungen - CubeServ<\/title>\n<meta name=\"description\" content=\"In Microsoft Fabric k\u00f6nnen strukturierte Daten auf verschiedene Wiesen 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