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aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f6bef61d09","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-a3490579b5","text":"\u003Cp\u003EDaten sind für die Weltwirtschaft ebenso wichtig wie Elektrizität. Aber ebenso wie wir Kraftwerke brauchen, um Haushalte und Maschinen mit Strom zu versorgen, brauchen wir Systeme, die Daten erfassen, speichern und organisieren, bevor sie eingesetzt werden können.\u003C/p\u003E\n\u003Cp\u003EData Warehouses führen Unmengen verschiedener Datentypen zusammen – Kundenprofile, Finanztransaktionen, Produktkataloge, Geräteprotokolle, Markttrends usw. – und machen es dann leicht, diese Daten auf verschiedenste Weise aufzuschlüsseln und zu analysieren. Mit Data Warehouses können CFOs den Umsatz für das nächste Jahr prognostizieren, Personalverantwortliche den Personalbedarf einschätzen, Betriebsleitungen ihre Fertigungsanlagen optimieren und CEOs strategische Entscheidungen über die Zukunft ihres Unternehmens treffen. Sie schaffen außerdem die Grundlage für neue KI-Tools, indem sie hochwertige Informationsquellen für das Training von KI-Modellen bereitstellen.\u003C/p\u003E\n\u003Cp\u003EIn diesem Artikel untersuchen wir, was Data Warehouses so einzigartig macht, welche Komponenten bei der Erstellung von Data Warehouses eine Rolle spielen und wie sie Organisationen einen Wettbewerbsvorteil verschaffen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4d03bc3f2d","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist ein Data Warehouse?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e748f577c0","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBei einem Data Warehouse handelt es sich um ein zentrales Repository, das aktuelle und historische Daten aus verschiedenen Quellen der gesamten Organisation speichert und damit Business Intelligence (BI) und Analytics unterstützt. Data Warehouses schaffen eine Single Source of Truth für Geschäftsdaten und eliminieren so Unstimmigkeiten und Duplikate, die entstehen, wenn verschiedene Abteilungen ihre eigenen Daten-Repositorys nutzen.\u003C/p\u003E\r\n\u003Cp\u003EIm Gegensatz zu operativen \u003Ca href=\"https://www.snowflake.com/de/fundamentals/database-management-system/\" target=\"_blank\"\u003EDatenbanken\u003C/a\u003E, die für die Abwicklung alltäglicher Transaktionen ausgelegt sind, sind Data Warehouses für komplexe Abfragen, Berichte und Datenanalytik optimiert, um die strategische Entscheidungsfindung zu unterstützen. Während eine operative oder relationale Datenbank nahezu in Echtzeit Informationen darüber enthalten kann, wie die verschiedenen Segmente des Unternehmens abschneiden, bieten Data Warehouses eine eher historisch ausgerichtete Perspektive auf die gesamte Organisation. Dadurch nutzen sie unterschiedliche Organisationsstrukturen und Methoden zur Abwicklung von Transaktionen.\u003C/p\u003E\r\n\u003Cp\u003EData Warehouses unterscheiden sich auch von \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-lakes-and-data-lake-architecture/\"\u003EData Lakes\u003C/a\u003E, in denen Rohdaten gespeichert werden, um sie für künftige Analysen zu verwenden. Ein Data Lake ähnelt einem Lagerraum, in dem Sie Daten ablegen, von denen Sie glauben, dass sie eines Tages benötigt werden. In einem Data Warehouse wissen Sie genau, was Sie haben und wie Sie es nutzen wollen. Ein Data Lake kann als Quelle für Rohdaten dienen, die später extrahiert, umgewandelt und in ein Data Warehouse geladen werden können.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/use-cases/data-lakehouse/\"\u003EData Lakehouses\u003C/a\u003E sind ein jüngerer Hybrid aus diesen beiden Konzepten. Sie kombinieren die Fähigkeit eines Data Lakes, strukturierte und unstrukturierte Daten zu relativ geringen Kosten zu speichern, mit den fortschrittlichen Analytikfunktionen eines Data Warehouse.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_2047987827":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_144795965":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1841052327":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_129745108":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-2c6596f86c","additionalClasses":"headline-decoration","type":"heading2","lines":["Architektur eines Data Warehouse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8d12b0ce84","text":"\u003Cp\u003EDie genauen Funktionen der einzelnen Data Warehouses können variieren, basieren jedoch in der Regel auf einer dreistufigen \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003EArchitektur\u003C/a\u003E, um strukturierte, unstrukturierte und semistrukturierte Daten schnell und effizient zu verarbeiten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-88f07075b6","additionalClasses":"headline-decoration","type":"heading4","lines":["Untere Ebene: Erfassung"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-d36716f802","text":"\u003Cp\u003EIn dieser Ebene werden Informationen aus internen und externen Datenquellen erfasst und gespeichert. Diese Daten werden aus ihrer ursprünglichen Quelle extrahiert und umgewandelt oder angereichert, indem Unstimmigkeiten beseitigt, Fehler korrigiert, Dateien in ein einheitliches Format konvertiert und Felder für Berechnungen erstellt werden. Anschließend werden sie in ein zentrales Repository geladen – ein Prozess, der als Extract, Transform, Load (ETL) bezeichnet wird. Modernere Architekturen nutzen die interne Verarbeitungsleistung des Warehouse, um die Daten nach dem Laden umzuwandeln, besser bekannt als \u003Ca href=\"https://www.snowflake.com/de/fundamentals/understanding-extract-load-transform-elt/\"\u003EELT\u003C/a\u003E.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1841052327":{"id":"title-v2-935f010797","additionalClasses":"headline-decoration","type":"heading4","lines":["Mittlere Ebene: Analyse"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_129745108":{"id":"text-6368185ec8","text":"\u003Cp\u003EAuf der mittleren Ebene finden \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-analytics/\" target=\"_blank\"\u003EDatenanalysen\u003C/a\u003E statt. Hier erfasst das Warehouse auch technische und betriebliche Metadaten, um die Datenherkunft (Data Lineage) nachzuverfolgen, seine Vertrauenswürdigkeit zu gewährleisten und Nutzenden zu helfen, zu verstehen, was die Daten bedeuten und wie sie verwendet werden können. Anschließend werden mit einer \u003Ca href=\"https://www.snowflake.com/de/fundamentals/olap-vs-oltp-the-differences/\"\u003EOLAP-Engine (Online Analytical Processing)\u003C/a\u003E Tausende von Datenzeilen gleichzeitig über mehrere Dimensionen hinweg analysiert. So könnten etwa Verkaufsdaten im Handel nach Artikel, erzieltem Preis, Warenkosten, Transaktionsdatum und -zeit, geografischer Lage, Filialidentität, Kundensegment und vielem mehr analysiert werden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_144795965":{"id":"title-v2-5efe40d692","additionalClasses":"headline-decoration","type":"heading4","lines":["Obere Ebene: Berichterstattung"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_2047987827":{"id":"text-3f3ae0f906","text":"\u003Cp\u003EAuf der letzten Ebene können Nutzende Ad-hoc-Analysen der Daten durchführen, z. B., indem sie Online-Verkäufe mit denen in den Verkaufsfilialen vergleichen oder die Performance verschiedener Kundensegmente in verschiedenen Regionen analysieren. Nutzende können diese Ergebnisse zur weiteren Analytik in Business-Intelligence-Tools oder Executive Dashboards exportieren.\u003C/p\u003E\n\u003Cp\u003ESo können Daten, die aus einem CRM- oder ERP-System (Customer Relationship Management oder Enterprise Resource Planning) stammen, in ein Data Warehouse eingespeist, bereinigt und standardisiert, für die Analyse optimiert, in kleinere domänenspezifische Repositorys (Data Marts) exportiert, oder direkt in BI-Plattformen oder Dashboards importiert werden, wo Nutzende Abfragen mit den Daten durchführen können.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_1841052327","text_copy_1241045613_129745108","title_v2_copy_copy_c_144795965","text_copy_1241045613_2047987827"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613_1221049664":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1482435266":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1740596496":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_2006380649":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1673136694":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1327337877":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1394849896":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_440491184":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1320012850":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_546504990":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1581037036":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1966069952":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1523315267":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_2019252551":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-606682fe67","additionalClasses":"headline-decoration","type":"heading2","lines":["Wichtige Komponenten eines Data Warehouse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1b71bf3dfe","text":"\u003Cp\u003EJedes Data Warehouse besteht aus denselben grundlegenden Komponenten. Ein Data Warehouse umfasst immer mindestens:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-913fb5388d","additionalClasses":"headline-decoration","type":"heading4","lines":["Datenquellen"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-f241183c9b","text":"\u003Cp\u003ENeben CRM-, ERP- und anderen Unternehmensdatenbanken können die Daten auch aus externen Quellen gewonnen werden, wie z. B. aus Marktforschung oder Echtzeit-Datenströme von Webapplikationen oder \u003Ca href=\"https://www.snowflake.com/de/fundamentals/extracting-insights-from-ioT-data/\"\u003EIoT-\u003C/a\u003ESensoren.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1482435266":{"id":"title-v2-99cbcfb48c","additionalClasses":"headline-decoration","type":"heading4","lines":["ETL/ELT-Prozesse"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_440491184":{"id":"text-cdf1c358ab","text":"\u003Cp\u003EDie Tools, die Daten aus jeder Quelle extrahieren, bereinigen und standardisieren und in das Warehouse laden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1966069952":{"id":"title-v2-abe795407d","additionalClasses":"headline-decoration","type":"heading4","lines":["Stagingbereich"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_2006380649":{"id":"text-39a3cf9cfa","text":"\u003Cp\u003EEine Pufferzone, in der Rohdaten zwischengespeichert, validiert und aufbereitet werden, bevor sie in das Hauptwarehouse gelangen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1523315267":{"id":"title-v2-246c4feed2","additionalClasses":"headline-decoration","type":"heading4","lines":["Datenspeicher"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1221049664":{"id":"text-66d32a3b4e","text":"\u003Cp\u003EDer Ort, an dem sich die Informationen physisch befinden und nach einem Schema organisiert sind – einem Framework, das beschreibt, wie die verschiedenen Daten miteinander in Beziehung stehen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_2019252551":{"id":"title-v2-86bae8057c","additionalClasses":"headline-decoration","type":"heading4","lines":["Metadaten-Managementsysteme"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1673136694":{"id":"text-0828b97fbd","text":"\u003Cp\u003ESysteme, die Informationen über die Struktur der Daten, die für sie geltenden Geschäftsregeln und die Historie der Daten während ihrer Umwandlung und Verwendung verwalten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1327337877":{"id":"title-v2-9abb6fd472","additionalClasses":"headline-decoration","type":"heading4","lines":["Abfrage- und Reporting-Tools"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1581037036":{"id":"text-ed55348dbe","text":"\u003Cp\u003EDie technische Infrastruktur, die es Nutzenden ermöglicht, Daten auf komplexe Weise abzufragen und differenzierte Antworten zu erhalten. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1394849896":{"id":"title-v2-7e587c427b","additionalClasses":"headline-decoration","type":"heading4","lines":["Data Governance und Sicherheitskontrollen"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1740596496":{"id":"text-11ec4f3a71","text":"\u003Cp\u003EDie Kontrollen zur Verwaltung der Benutzerauthentifizierung und Zugriffsberechtigungen, zum Schutz sensibler Daten und zur Prüfung der Einhaltung von Sicherheitsrichtlinien.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_546504990":{"id":"title-v2-cea3848e0d","additionalClasses":"headline-decoration","type":"heading4","lines":["Analytics und BI-Anwendungen"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1320012850":{"id":"text-9f25674925","text":"\u003Cp\u003EExterne Tools, mit denen Nutzende ausführliche Analysen durchführen, Berichte erstellen und die Ergebnisse visualisieren können.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_1482435266","text_copy_1241045613_440491184","title_v2_copy_copy_c_1966069952","text_copy_1241045613_2006380649","title_v2_copy_copy_c_1523315267","text_copy_1241045613_1221049664","title_v2_copy_copy_c_2019252551","text_copy_1241045613_1673136694","title_v2_copy_copy_c_1327337877","text_copy_1241045613_1581037036","title_v2_copy_copy_c_1394849896","text_copy_1241045613_1740596496","title_v2_copy_copy_c_546504990","text_copy_1241045613_1320012850"]},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1200247337":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_27886980":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_188292923":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1166658867":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-8f148b145a","additionalClasses":"headline-decoration","type":"heading2","lines":["Arten von Data Warehouses"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-be6eacaf54","text":"\u003Cp\u003ENicht alle Data Warehouses sind gleich. Einige sind darauf ausgelegt, die langfristige strategische Planung zu unterstützen, andere darauf, den taktischen Betrieb zu managen. Darüber hinaus gibt es Teilmengen von Data Warehouses, die für bestimmte Gruppen innerhalb einer Organisation erstellt wurden. Sie werden in drei Hauptarten unterteilt: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/enterprise-data-warehouse/\"\u003EEnterprise Data Warehouses (EDW)\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/fundamentals/a-modern-approach-to-the-operational-data-store/\"\u003EOperational Data Stores (ODS)\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003EData Marts\u003C/a\u003E.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-b9f0d720a4","additionalClasses":"headline-decoration","type":"heading4","lines":["Enterprise Data Warehouse"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-244a6c9953","text":"\u003Cp\u003EBei einem EDW handelt es sich um ein zentrales Repository, in dem Daten aus allen wichtigen Geschäftssystemen integriert werden. So entsteht eine umfassende Übersicht über das gesamte Unternehmen. Es kombiniert Daten aus verschiedenen Systemen in einem einheitlichen Format, kann historische Daten aus mehreren Jahren enthalten und unterstützt die Entscheidungsfindung sowie die strategische Planung in allen Unternehmensfunktionen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1166658867":{"id":"title-v2-f04a05be0b","additionalClasses":"headline-decoration","type":"heading4","lines":["Operational Data Store"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1200247337":{"id":"text-521194bcf1","text":"\u003Cp\u003EEin ODS soll die Lücke zwischen Transaktionssystemen schließen, die Echtzeitdaten speichern, und Data Warehouses, die für langfristige Analytik und Strategien verwendet werden. Dabei werden die Daten in ihrem ursprünglichen Format gespeichert, ohne dass ETL- oder ELT-Prozesse erforderlich sind. Da er für die tägliche Entscheidungsfindung und Echtzeitanalyse entwickelt wurde, wird ein ODS häufiger aktualisiert und enthält weniger historische Daten als ein EDW.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_188292923":{"id":"title-v2-c248894b93","additionalClasses":"headline-decoration","type":"heading4","lines":["Data Mart"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_27886980":{"id":"text-a0928c86b7","text":"\u003Cp\u003EBei einem Data Mart handelt es sich üblicherweise um eine eng begrenzte Teilmenge eines EDW, die auf bestimmte Geschäftsbereiche wie Vertrieb, Marketing, Finanzen oder Personalwesen beschränkt ist. Jeder Data Mart wurde für die analytischen Anforderungen einer bestimmten Geschäftsfunktion optimiert und ist meistens schneller zu implementieren und aufgrund seines begrenzten Umfangs einfacher zu pflegen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_1166658867","text_copy_1241045613_1200247337","title_v2_copy_copy_c_188292923","text_copy_1241045613_27886980"]},"container_copy_copy__987904085":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1336603468":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1789862769":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-2ebeeed176","additionalClasses":"headline-decoration","type":"heading2","lines":["Klassische vs. Cloud Data Warehouses"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-f0db096434","text":"\u003Cp\u003EWie bei anderen geschäftskritischen IT-Funktionen können Organisationen selbst bestimmen, wie und wo sie ein Data Warehouse bereitstellen möchten. Unternehmen können ihr eigenes Data Warehouse in einem On-Premise-Rechenzentrum unterhalten, in der Cloud hosten lassen oder eine hybride Architektur implementieren, die beides kombiniert. Wie die folgende Tabelle zeigt, gibt es erhebliche Unterschiede zwischen der lokalen (on-premise) und der cloudbasierten Option hinsichtlich Infrastrukturinvestitionen, Performance, Skalierbarkeit, Wartung und Kosteneffizienz.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_1241045613_1336603468":{"id":"text-1d4e1cc189","text":"\u003Ctable\u003E\r\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EBetrieblicher Aspekt\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EData Warehouse – On-Premises\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EData Warehouse ;– Cloud\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EInfrastruktur\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EUnternehmen kaufen, installieren und pflegen sämtliche Hard- und Softwaresysteme und verwalten so den gesamten IT-Stack.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EAnbieter verwalten die gesamte physische Infrastruktur; Unternehmen können die Infrastruktur über APIs und Webschnittstellen steuern. \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EPerformance\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EDefiniert durch spezifische Hardware-Konfigurationen, die Teams für bestimmte Workloads optimieren können. Minimale Netzwerklatenz\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EElastische Performance kann je nach Workload-Anforderung hoch- oder herunterskaliert werden. Latenzprobleme und die Notwendigkeit, Ressourcen mit anderen Cloud-Mandanten zu teilen, können die Performance beeinträchtigen. \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ESkalierbarkeit\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EDie Erweiterung der Warehouse-Kapazitäten erfordert erhebliche Vorausplanung und Kapitalinvestitionen und kann zu einer Überbereitstellung führen, wenn die Workload-Anforderungen abnehmen.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003ESpeicher- und Rechenressourcen können mithilfe eines \u003Ca href=\"https://www.snowflake.com/de/pricing-options/\"\u003Everbrauchsbasierten Preismodells\u003C/a\u003E nahezu unbegrenzt je nach Workload-Anforderung skaliert werden. \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EWartung\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EUnternehmen sind für alle Wartungen, Patches und Upgrades verantwortlich, was erhebliche Personalinvestitionen erfordert. \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EAnbieter übernehmen die gesamte Infrastrukturwartung, während Unternehmen für Data Governance, Sicherheit und die Anwendungswartung verantwortlich sind.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EKosten\u003C/p\u003E\r\n\u003Cbr\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EErfordert erhebliche Vorabinvestitionen sowie laufende Betriebskosten, aber die Kosten bleiben unabhängig von der tatsächlichen Nutzung relativ fix und vorhersehbar.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EKosten können direkt mit der Nutzung skaliert werden, was zu unvorhersehbaren Ausgaben und hohen Gebühren für die Datenübertragung führen kann. \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_1241045613_1789862769":{"id":"text-28050b07e8","text":"\u003Cp\u003EUnternehmen, die sich für On-Premise-Lösungen entscheiden, verfügen in der Regel über starke IT-Organisationen, stellen vorhersehbare, große Workloads bereit und benötigen vollständige Kontrolle über Datenhoheit, Governance und Infrastruktur. Organisationen entscheiden sich für Cloud-Anbieter, wenn ihnen internes Fachwissen fehlt, die Bereitstellung schnell erfolgen muss, die Workloads stark variieren, globaler Zugriff auf Daten erforderlich ist oder die Investitionsausgaben im Voraus minimiert werden sollen.\u003C/p\u003E\n\u003Cp\u003EViele Organisationen beginnen mit der Einführung hybrider Ansätze, bei denen die Kontrolle über sensible Daten vor Ort mit der Skalierbarkeit der Cloud für Analytik- und Entwicklungs-Workloads kombiniert wird. So können sie sowohl Sicherheit als auch Agilität anhand spezifischer Anforderungen für individuelle Anwendungsfälle optimieren.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text_copy_1241045613","text_copy_1241045613_1336603468","text_copy_1241045613_1789862769"]},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1482435266":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_440491184":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1685140297":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_2006380649":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_234872856":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1919868179":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1966069952":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_27696057":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e9113f2d18","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile eines modernen Data Warehouse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d53a30823f","text":"\u003Cp\u003EEs gibt viele Gründe, warum sich Organisationen für die Bereitstellung eines Data Warehouse entscheiden. Hier einige der wichtigsten:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-f5884e3b8f","additionalClasses":"headline-decoration","type":"heading4","lines":["Bessere Entscheidungsfindung"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-49c0e82d80","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAls Single Source of Truth stellen Data Warehouses sicher, dass alle innerhalb einer Organisation über die genauesten und aktuellen Informationen für ihre Entscheidungsfindung verfügen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1482435266":{"id":"title-v2-5a3f99a07a","additionalClasses":"headline-decoration","type":"heading4","lines":["Analytik auf historischen Daten und Trends"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_440491184":{"id":"text-e559dbbbec","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDank der Möglichkeit, historische Daten über viele Jahre hinweg zu analysieren, können Organisationen langfristige Trends und betriebliche Verhaltensmuster erkennen, was wiederum bei der strategischen Planung hilfreich ist.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1966069952":{"id":"title-v2-bbac72fb57","additionalClasses":"headline-decoration","type":"heading4","lines":["Verbesserte Datenqualität und -konsistenz"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_2006380649":{"id":"text-50d062b059","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDie integrierten Validierungs- und Bereinigungsfunktionen eines Data Warehouse gewährleisten, dass die Unternehmensdaten zuverlässig und vollständig sind.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1919868179":{"id":"title-v2-20c7dd931c","additionalClasses":"headline-decoration","type":"heading4","lines":["Skalierbarkeit für große Datenvolumina"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1685140297":{"id":"text-d1534cf695","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ECloudbasierte Data Warehouses können Rechen- und Speicherressourcen automatisch bedarfsabhängig skalieren und analytische Workloadspitzen bewältigen, ohne zu viel bereitzustellen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_234872856":{"id":"title-v2-cd67573b55","additionalClasses":"headline-decoration","type":"heading4","lines":["Unterstützung für erweiterte Analytik und KI"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_27696057":{"id":"text-6e198a4b69","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDie meisten Data Warehouses unterstützen erweiterte Analytik-Funktionen wie Regressionsanalysen und \u003Ca href=\"https://www.snowflake.com/de/fundamentals/time-series-analysis/\" target=\"_blank\"\u003EZeitreihenprognosen\u003C/a\u003E. Einige verfügen auch über integrierte Funktionen für \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emaschinelles Lernen\u003C/a\u003E, mit denen Data Scientists Modelle direkt in der Warehouse-Umgebung verwalten können.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_1482435266","text_copy_1241045613_440491184","title_v2_copy_copy_c_1966069952","text_copy_1241045613_2006380649","title_v2_copy_copy_c_1919868179","text_copy_1241045613_1685140297","title_v2_copy_copy_c_234872856","text_copy_1241045613_27696057"]},"container_copy_copy__127842237":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_574634569":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1065857477":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1087831612":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_584800053":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_570690091":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_2134439506":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9ab9fc9274","additionalClasses":"headline-decoration","type":"heading2","lines":["Häufig gestellte Fragen zum Data Warehouse Management"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"title_v2_copy_copy_c":{"id":"title-v2-eaf66cf77a","additionalClasses":"headline-decoration","type":"heading4","lines":["Wie unterscheidet sich ein Data Warehouse von einer regulären Datenbank?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-dbb82ea1e9","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EGewöhnliche Datenbanken werden für die alltäglichen Transaktionen einer Organisation entwickelt, während Data Warehouses für analytische Abfragen historischer Daten ausgelegt sind und sich somit ideal für strategische Entscheidungen eignen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_2134439506":{"id":"title-v2-f32c82d2c9","additionalClasses":"headline-decoration","type":"heading4","lines":["Welche Daten werden in einem Data Warehouse gespeichert?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1065857477":{"id":"text-a1273f70e8","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EData Warehouses können mehrere Arten von strukturierten, unstrukturierten und semistrukturierten Daten aus einer Vielzahl von Quellen speichern, wie interne Unternehmensdatenbanken, externe Marktberichte und Echtzeit-Datenströme.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_584800053":{"id":"title-v2-b02babbd07","additionalClasses":"headline-decoration","type":"heading4","lines":["Welche Probleme lösen Data Warehouses für Organisationen?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_570690091":{"id":"text-149b3f3c54","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAls Single Source of Truth beseitigen Data Warehouses Datensilos, gewährleisten abteilungsübergreifende Datenkonsistenz, unterstützen Analytik auf historischen Daten, verbessern die Performance von Abfragen für die Analytik und liefern zuverlässige Berichtsfunktionen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1087831612":{"id":"title-v2-9a95f4d1bc","additionalClasses":"headline-decoration","type":"heading4","lines":["Wie sicher sind Data Warehouses?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_574634569":{"id":"text-93db5e1276","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EModerne Data Warehouses erfüllen die Sicherheitsanforderungen von Unternehmen, einschließlich Verschlüsselung, Zugriffskontrolle, Prüfprotokolle und Compliance-Zertifizierungen. Cloud-Anbieter stellen oft Sicherheitsfunktionen zur Verfügung, die über das hinausgehen, was die meisten Organisationen selbstständig implementieren könnten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":type":"snowflake-site/components/container",":itemsOrder":["title_v2","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_2134439506","text_copy_1241045613_1065857477","title_v2_copy_copy_c_584800053","text_copy_1241045613_570690091","title_v2_copy_copy_c_1087831612","text_copy_1241045613_574634569"]},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn 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