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18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/de/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-61c24dcf83",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7e1f151036","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-81d0bb5367","text":"\u003Cp\u003ESo wie Geld der Rohstoff für das globale Finanzsystem ist, sind Daten die Währung für moderne Unternehmen, insbesondere für solche, die sich mit Analytics und KI beschäftigen.\u003C/p\u003E\n\u003Cp\u003EDatenorchestrierung bildet das Rückgrat des \u003Ca href=\"https://www.snowflake.com/de/product/data-engineering/\"\u003Emodernen Data Engineerings\u003C/a\u003E, denn sie schafft eine Möglichkeit, den Datenfluss, auch bekannt als \u003Ca href=\"https://www.snowflake.com/de/product/use-cases/pipelines/\"\u003EDaten-Pipeline\u003C/a\u003E, zu automatisieren und zu verwalten. Der Prozess umfasst die Organisation, \u003Ca href=\"https://www.snowflake.com/de/blog/better-data-pipelines-sql-python/\"\u003EUmwandlung\u003C/a\u003E und Aktivierung von Daten, um sie für Geschäftsanwendungen nutzbar zu machen, was wiederum die Grundlage für eine fundierte Entscheidungsfindung bildet.\u003C/p\u003E\n\u003Cp\u003EIm Bankwesen können Sie sich Datenorchestrierung wie eine riesige virtuelle Kasse vorstellen, die Währungen aus allen Ländern enthält, numerisch nach Wert und alphabetisch nach Nation geordnet. Indem die Verwaltung und Organisation dieses Geldes automatisiert wird, wird das Geld bei jedem neuen Geldeingang automatisch nach Nennwert sortiert und die Geldbeträge werden nach Ländern aktualisiert.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-b9e01190ff","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Datenorchestrierung?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-04cbd854fa","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/how-native-apps-simplified-mdo-customers-tech-orchestration/\"\u003EDatenorchestrierung\u003C/a\u003E umfasst automatisierte Aktivitäten und Prozesse rund um die Koordination und Verwaltung komplexer Daten-Pipelines und Workflows. Dabei werden verschiedenste Daten gesammelt, umgewandelt und verarbeitet, damit sie für KI und Analytics genutzt werden können. Der Automatisierungsteil dieser Definition ist hierbei entscheidend. Große Unternehmen verfügen über riesige Datenbestände, die oft auf die Gruppen aufgeteilt sind, die sie täglich erzeugen und nutzen. Der Einsatz von Orchestrierungstools zur Automatisierung verschiedener Aufgaben innerhalb von Daten-Workflows sorgt für einen geordneten und einheitlichen Umgang mit Rohdaten, verbessert die Datenqualität und -verfügbarkeit und unterstützt gleichzeitig die Einhaltung von Datensicherheitsbestimmungen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-cad29f11bb","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum ist Datenorchestrierung wichtig?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-6b5e8e1b47","text":"\u003Cp\u003EDie meisten datengestützten Unternehmen können von Datenorchestrierung profitieren, da die \u003Ca href=\"https://www.snowflake.com/en/blog/reimagine-batch-streaming-data-pipelines/\"\u003EAutomatisierung von Daten-Pipelines\u003C/a\u003E die Nutzbarmachung von Daten aus verschiedenen Quellen und Systemen innerhalb des Unternehmens beschleunigt und gleichzeitig die Qualität und Maschinenlesbarkeit der Daten verbessert.\u003C/p\u003E\n\u003Cp\u003EJe größer ein Unternehmen ist, desto komplexer ist wahrscheinlich auch sein Datenuniversum. Die Daten können mittels Datenorchestrierung für die \u003Ca href=\"https://www.snowflake.com/de/blog/ai-data-agents-snowflake-cortex/\"\u003ENutzung in KI- und Analytics-Systemen\u003C/a\u003E aufbereitet werden. Diese Systeme fördern die Entscheidungsfindung, arbeiten effizienter, sorgen für Datensicherheit und verbessern die Kundenbetreuung. Einfach ausgedrückt: Datenorchestrierung ist für die Bewältigung der betrieblichen Komplexität, die Gewährleistung der Zuverlässigkeit und die Stärkung des Stakeholder-Vertrauens von zentraler Bedeutung.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-c78973b367","additionalClasses":"headline-decoration","type":"heading2","lines":["Die 3 Schritte der Datenorchestrierung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-93f4fb274b","text":"\u003Cp\u003EBei Datenorchestrierung geht es nicht nur um die Speicherung oder Verwaltung von Daten. Es handelt sich um eine Reihe von Prozessen, bei denen Tools mit Rohdaten aus unterschiedlichen Quellen arbeiten und diese für verschiedene datengestützten Geschäftsanwendungen aufbereiten. Im Einzelnen:\u003C/p\u003E\r\n\u003Ch4\u003ESchritt 1: Organisation der Daten\u003C/h4\u003E\r\n\u003Cp\u003EDer erste Schritt bei der Datenorchestrierung besteht in der Beschaffung von Daten aus den relevanten Systemen und Quellen und der anschließenden Zentralisierung dieser Daten. Bei den Datenquellen kann es sich z. B. um interne CRM- oder ERP-Systeme handeln, die in einem Rechenzentrum vor Ort oder in der Cloud angesiedelt sind, oder um externe Quellen, wie soziale Medien oder Nachrichten-Websites. Zur Organisation von Daten gehört es, ähnliche Daten an einem Ort zusammenzufassen – beispielsweise alle Verkaufsdaten oder alle Telefonnummern potenzieller Neukund:innen –, damit sie im nächsten Schritt optimiert werden können.\u003C/p\u003E\r\n\u003Ch4\u003ESchritt 2: Datenumwandlung\u003C/h4\u003E\r\n\u003Cp\u003EBei der Umwandlung standardisiert das Orchestrierungstool Rohdaten, indem Geschäftsregeln und eine einheitliche Formatierung angewendet werden. Bei fehlenden Informationen muss das Tool die Daten möglicherweise anreichern, indem es die Lücken füllt und Fehler korrigiert. Das Ziel ist die Bereitstellung präziser, einheitlicher Daten in einem Standardformat, damit ML-Modelle und Analytiktools sie leichter nutzen können. So können Unternehmen diese Daten bearbeiten und die Informationen über Dashboards und Visualisierungstools teilen.\u003C/p\u003E\r\n\u003Ch4\u003ESchritt 3: Datenaktivierung\u003C/h4\u003E\r\n\u003Cp\u003ESobald Sie die Daten so normalisiert haben, dass sie in einem lesbaren Format vorliegen und einigermaßen fehlerfrei sind, müssen Sie sie den Systemen zugänglich machen, in denen die Daten analysiert werden oder die sie für Visualisierungs- und Dashboard-Tools bereitstellen. Dieser Vorgang wird als Datenaktivierung bezeichnet.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bc60b63282","additionalClasses":"headline-decoration","type":"heading2","lines":["5 Vorteile von Datenorchestrierungstools"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-db0bfb40d6","text":"\u003Cp\u003EDatenorchestrierungstools erleichtern den Zugang zu Daten für die Entscheidungsfindung in Unternehmen, für Datenanalysen oder für KI-Anwendungen. Mit diesen Tools können Sie die oben beschriebenen Schritte automatisieren und einen höheren Wert aus den geschäftlichen Rohdaten ziehen. Und so benötigen Sie weniger Zeit, um Daten in Erkenntnisse zu verwandeln. Sehen wir uns die Vorteile an:\u003C/p\u003E\r\n\u003Ch4\u003E1. Erhöhte Zuverlässigkeit und Einheitlichkeit\u003C/h4\u003E\r\n\u003Cp\u003EWenn Sie bereinigte, genaue Daten in Systeme für \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emaschinelles Lernen \u003C/a\u003Eund Datenanalyse einspeisen, erhalten Sie zuverlässigere, beständigere Ergebnisse.\u003C/p\u003E\r\n\u003Ch4\u003E2. Verbesserte Effizienz und Skalierbarkeit\u003C/h4\u003E\r\n\u003Cp\u003EJe größer und komplexer ein Unternehmen ist, desto mehr Daten erzeugt es. Datenorchestrierungstools können dazu beitragen, Datensilos aufzubrechen und den Zugriff auf diese Daten zu erleichtern. Sie unterstützen Sie auch bei der Verbesserung der Datenqualität, was für die Skalierung datengestützter Initiativen und anhaltende Agilität unerlässlich ist.\u003C/p\u003E\r\n\u003Ch4\u003E3. Verbesserte Transparenz und Überwachung\u003C/h4\u003E\r\n\u003Cp\u003ETools, die die Datenorchestrierung automatisieren und optimieren, helfen Ihnen, zu bestimmten Informationen ins Detail zu gehen, erleichtern die Erkennung von Anomalien und verbessern die Datenqualität im Laufe der Zeit.\u003C/p\u003E\r\n\u003Ch4\u003E4. Schnellere Time-to-Data\u003C/h4\u003E\r\n\u003Cp\u003ETime-to-Data (TTD) bezieht sich auf die Zeit, die von der Erstellung der Daten bis zu dem Zeitpunkt vergeht, an dem Sie sie als geschäftliches Asset verwenden können. (Manchmal wird dieser Faktor auch als „Datenlatenz“ bezeichnet.) Mit Datenorchestrierungstools können Sie die Latenz verringern, wodurch die Datenverarbeitung einfacher und schneller wird. Eine kürzere TTD verringert auch die Zeit, die es braucht, um Mehrwert aus Daten zu gewinnen, wodurch der gesamte Entscheidungsprozess beschleunigt wird.\u003C/p\u003E\r\n\u003Ch4\u003E5. Leichtere Einhaltung von Datenbestimmungen\u003C/h4\u003E\r\n\u003Cp\u003EWeltweit tätige Unternehmen unterliegen Vorschriften zur Datensicherheit und zum Datenschutz, wie z. B. DSGVO, HIPAA und CCPA. Datenorchestrierungstools tragen dazu bei, bestimmte Datensicherheitskontrollen zu automatisieren, um die Einhaltung dieser Regeln zu vereinfachen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__987904085":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-eba58d2314","additionalClasses":"headline-decoration","type":"heading2","lines":["Herausforderungen der Datenorchestrierung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-172e4e3c0e","text":"\u003Cp\u003EDatenorchestrierung birgt auch einige potenzielle Fallstricke, die durch das ständig wachsende Datenvolumen und die -komplexität noch verschärft werden können. Sehen wir uns drei typische Herausforderungen an:\u003C/p\u003E\r\n\u003Ch4\u003E1. Hohe Komplexität der Tools\u003C/h4\u003E\r\n\u003Cp\u003EDie Datenorchestrierungstools selbst können schwierig zu handhaben sein – ebenso wie die Tools, die Teams zur Automatisierung von Teilen des Prozesses verwenden. Außerdem arbeiten die Tools nicht immer problemlos zusammen, sodass die Integration aller Tools möglicherweise eine Anpassung der Software oder eine Änderung einiger Datenverarbeitungsprozesse erfordert, um den spezifischen Anforderungen gerecht zu werden.\u003C/p\u003E\r\n\u003Ch4\u003E2. Verwaltung von Datenabhängigkeiten\u003C/h4\u003E\r\n\u003Cp\u003EBei der Programmierung beziehen sich Abhängigkeiten auf Aufgaben, die in einer bestimmten Reihenfolge ausgeführt werden müssen. Abhängigkeiten treten auf, wenn ein Schritt in einem linearen Prozess nur dann ausgeführt werden kann, wenn die Ergebnisse eines vorangegangenen Vorgangs vorliegen. Je komplexer die Datenabläufe sind, desto mehr Abhängigkeiten müssen beachtet werden. Data Engineers setzen gerichtete azyklische Graphen (Directed Acyclic Graphs, DAGs) ein, um Datenflüsse visuell darzustellen und Abhängigkeiten zu veranschaulichen.\u003C/p\u003E\r\n\u003Ch4\u003E3. Probleme mit der Skalierbarkeit\u003C/h4\u003E\r\n\u003Cp\u003EJe mehr Daten Sie aus verschiedenen Systemen erhalten, desto schwieriger wird es, alle erforderlichen Prozesse zu optimieren, um diese Daten durch die verschiedenen Schritte der Datenorchestrierung zu führen. Mit anderen Worten: Die Datenspeicherung muss skaliert werden, ebenso wie die Orchestrierungsfunktion selbst.\u003C/p\u003E\r\n\u003Ch4\u003E4. Begrenzte Integrationsmöglichkeiten\u003C/h4\u003E\r\n\u003Cp\u003EDatensysteme – und die Daten selbst – sind nicht immer leicht zu integrieren. Wie bereits erwähnt, arbeiten Datensysteme nicht immer gut zusammen, ohne dass sie angepasst oder umgerüstet werden müssen, und die Integration von Daten aus verschiedenen Quellen ist nicht immer unkompliziert, wobei inkompatible Datenformate ein häufiges Problem darstellen. Möglicherweise stoßen Sie auch auf Hindernisse bei der Kombination von Daten aus Altsystemen mit neueren Daten, die für \u003Ca href=\"https://www.snowflake.com/de/product/platform/\"\u003Emoderne, cloudbasierte Datenplattformen\u003C/a\u003E optimiert wurden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"implement",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-8fde0e8faf","additionalClasses":"headline-decoration","type":"heading2","lines":["Implementierung von Datenorchestrierung: 5 Best Practices"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-e506549253","text":"\u003Cp\u003EDie Befolgung der Best Practices für die Datenorchestrierung kann dazu beitragen, viele der oben genannten Herausforderungen zu meistern. Folgende fünf wichtige Tipps sollten Sie beachten:\u003C/p\u003E\r\n\u003Ch4\u003E1. Definieren Sie eindeutige Workflows\u003C/h4\u003E\r\n\u003Cp\u003EDaten-Workflows definieren, wie Daten in den IT-Systemen des Unternehmens bewegt und umgewandelt werden. Eine eindeutige Definition der Abläufe und Schritte, aus denen sich dieser Prozess zusammensetzt – von der Extraktion über die Organisation bis hin zur Aktivierung – wird Ihnen zum Erfolg verhelfen.\u003C/p\u003E\r\n\u003Ch4\u003E2. Wählen Sie das richtige Orchestrierungstool\u003C/h4\u003E\r\n\u003Cp\u003EEbenso müssen Sie das Datenorchestrierungstool auswählen, das am besten zu den allgemeinen Unternehmens- und den spezifischen Projektanforderungen passt. Achten Sie bei dieser Entscheidung auf die Benutzerfreundlichkeit des Tools, auf die Kompatibilität mit Ihren bestehenden Systemen und darauf, ob – und wie gut – es komplexe Workflows verarbeiten kann.\u003C/p\u003E\r\n\u003Ch4\u003E3. Implementieren Sie eine umfassende Überwachung\u003C/h4\u003E\r\n\u003Cp\u003EWie können Sie feststellen, ob die Datenorchestrierung funktioniert? Indem Sie den Prozess überwachen, können Sie nach Engpässen, Fehlern bei der Datenverarbeitung und Möglichkeiten zur Optimierung suchen.\u003C/p\u003E\r\n\u003Ch4\u003E4. Beginnen Sie mit einer kleinen Lösung und iterieren Sie\u003C/h4\u003E\r\n\u003Cp\u003EWenn Sie gerade erst mit der Datenorchestrierung beginnen, sollten Sie Ihren Umfang für die ersten Projekte eingrenzen. Nutzen Sie Proofs of Concept, um zu bewerten, wie gut Ihre Workflows und Tools funktionieren. Dann iterieren Sie mit verschiedenen Verbesserungen, um zu lernen, was am besten funktioniert.\u003C/p\u003E\r\n\u003Ch4\u003E5. Dokumentieren und verbessern Sie die Datenorchestrierung\u003C/h4\u003E\r\n\u003Cp\u003EEs ist immer empfehlenswert, die Prozesse in den Datenorchestrierungs-Workflows zu dokumentieren. Mit diesen Aufzeichnungen ist es einfach, Workflows regelmäßig zu überprüfen, Fehler zu dokumentieren und notwendige Änderungen vorzunehmen.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-none snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1985496925":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f3df3c5957","additionalClasses":"headline-decoration","type":"heading2","lines":["Fazit"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-0d480a6607","text":"\u003Cp\u003EDatenorchestrierung unterstützt Sie dabei, Daten erfolgreich für strategische Verbesserungen im Unternehmen zu nutzen. Außerdem ist die Fähigkeit, Workflows für die Erfassung, Umwandlung und Aktivierung von Daten zu definieren und zu automatisieren, ein Markenzeichen jedes leistungsstarken, datengestützten Unternehmens. Dabei geht Datenorchestrierung über bloße Datenverwaltung hinaus und schafft intelligente, integrierte Systeme, die die Komplexität reduzieren und dazu beitragen, dass Sie schneller und zuverlässiger einen Mehrwert aus den Daten ziehen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__127842237":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-cb476476e2","additionalClasses":"headline-decoration","type":"heading2","lines":["Häufig gestellte Fragen zur Datenorchestrierung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-f7d9714e5b","showDivider":false,"accordionItemsList":[{"title":"Was sind Datenorchestrierungstools?","richText":"\u003Cp\u003EDatenorchestrierungstools automatisieren den Prozess der Datenorchestrierung. Sie lassen sich in andere Datenverarbeitungs- und -visualisierungstools integrieren, um die Gewinnung von Erkenntnissen aus Rohdaten zu beschleunigen.\u003C/p\u003E\n"},{"title":"Welche Beispiele gibt es für Datenorchestrierung?","richText":"\u003Cp\u003EWenn Sie schon einmal einen Medienstreaming-Service genutzt haben, haben Sie Datenorchestrierung in Aktion erlebt. Denn die Medien, die Sie nutzen, bestehen aus Daten, und die Services nutzen Orchestrierung, um Ihnen diese Daten bereitzustellen – auf Grundlage Ihrer Suchanfragen, Ihrer Vorlieben und der Inhalte, die Sie geliked oder Ihrer Watchlist hinzugefügt haben.\u003C/p\u003E\n\u003Cp\u003EEin weiteres Beispiel, das Sie wahrscheinlich selbst erlebt haben: Wenn \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/retail-consumer-goods/\"\u003EHandelsunternehmen\u003C/a\u003E die Bestände von Onlineshops und Ladengeschäften synchronisieren müssen, nutzen sie die Orchestrierung, um Verkaufsdaten von beiden Standorten abzurufen. Dazu gehören auch aktualisierte Bestandsdaten von Ladengeschäften und Warenlagern, die als Quelle für Onlineverkäufe dienen. 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