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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":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":"fundamentals-main-content",":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-v2-16ff542b0d","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-6d2e36c371","text":"\u003Cp\u003EBig Data Analytics ist ein revolutionärer Ansatz zur Verarbeitung großer, komplexer Datasets. Auch wenn es zunächst trocken klingt, öffnet die Analytik Türen für wertvolle Erkenntnisse: Trends aufdecken, Abläufe optimieren und branchenübergreifende datengestützte Entscheidungsfindung ermöglichen. Die Möglichkeit, schnell Informationen aus großen Sammlungen strukturierter und unstrukturierter Daten zu analysieren und zu gewinnen, kann zu Fortschritten und Durchbrüchen – vom Gesundheitswesen bis hin zur Fertigung – führen und hat dies auch bereits getan. \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":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-30ba0ccc0a","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Big Data Analytics?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-60d9c9f69c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EBig Data Analytics ist der Prozess, bei dem riesige, vielfältige Datasets schnell erfasst und analysiert werden, um aussagekräftige kommerzielle oder wissenschaftliche Erkenntnisse zu gewinnen. Big Data-Analytics-Dienste lösen die Herausforderungen, die Daten mit sich bringen, die in extremer Menge und Geschwindigkeit in verschiedenen Formaten (strukturiert, semistrukturiert und unstrukturiert) eingehen. Durch den Einsatz skalierbarer, cloudnativer Rechenleistung können mithilfe von Analytics prädiktive Einblicke und Trends gewonnen werden, die für veraltete Verarbeitungssysteme unsichtbar wären. Letztlich können Unternehmen so von reaktivem Reporting zu proaktiver, datengestützter Strategie und überragender Entscheidungsfindung gelangen. \u003C/p\u003E\n\u003Cp\u003EBig Data Analytics unterscheidet sich grundlegend von herkömmlicher Data Analytics, indem sie sich auf den Umfang und die Komplexität der Daten konzentriert, anstatt nur Zusammenfassungen abzugeben. Während herkömmliche Methoden auf begrenzte, strukturierte Stichproben zurückgreifen, um vergangene Ereignisse zu analysieren, verarbeitet Big Data Analytics die Gesamtheit der multiformatigen Hochgeschwindigkeitsdaten, um Vorhersagemodelle zu erstellen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDie fünf Vs von Big Data Analytics\u003C/h3\u003E\n\u003Cp\u003EWährend Volumen, Geschwindigkeit und Vielfalt traditionell die Komplexität von Big Data definieren, erstreckt sich die moderne Definition auf die fünf Vs, um die wesentlichen Herausforderungen und notwendigen Ergebnisse von Big Data-Analysen vollständig zu erfassen. Diese zusätzlichen Faktoren – Richtigkeit und Wert – bestimmen, ob die Daten vertrauenswürdig und letztlich rentabel für das Unternehmen sind.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVolume (Volumen)\u003C/b\u003E: Das bezieht sich auf den schieren Umfang der täglich generierten Daten – denken Sie an Petabytes oder sogar Exabytes. Herkömmliche Datenbanken können diese Menge an Informationen einfach nicht speichern oder effizient abfragen.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVelocity (Geschwindigkeit)\u003C/b\u003E: Dies ist die Geschwindigkeit, mit der Daten erstellt, erfasst und verarbeitet werden. In vielen modernen Anwendungsfällen wie Betrugserkennung oder Aktienhandel müssen Einblicke nahezu in Echtzeit, oft innerhalb von Millisekunden, gewonnen werden.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVariety (Vielfalt)\u003C/b\u003E: Daten gibt es heute in verschiedenen Formaten, darunter Transaktionsdatensätze (strukturiert), Sensordaten und Webprotokolle (semistrukturiert) sowie Social-Media-Beiträge oder -Videos (unstrukturiert). Effektive Analytik muss in der Lage sein, all diese verschiedenen Datentypen zusammen zu harmonisieren und zu analysieren.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EVeracity (Richtigkeit)\u003C/b\u003E: Daten müssen vertrauenswürdig und genau sein, um sie für Analytics oder KI-Modelle zu verwenden. Denn sonst könnten sie zu schädlichen Ergebnissen führen. Die Verwendung von Daten mit niedriger Richtigkeit erfordert robuste Prozesse zur Datenbereinigung, Governance und Qualitätssicherung, um zu gewährleisten, dass die gewonnenen analytischen Erkenntnisse zuverlässig und nicht irreführend sind.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EValue (Wert)\u003C/b\u003E: Mehrwert ist das ultimative Ziel von Big Data Analytics: aussagekräftige, nützliche und rentable Einblicke aus riesigen Datasets zu gewinnen. Wenn ein Unternehmen erfolgreich Volumen, Geschwindigkeit, Vielfalt und Richtigkeit erreichen kann, dann ist der Wert das resultierende Geschäftsergebnis. Der Wert beinhaltet Wettbewerbsvorteile, Kosteneinsparungen, optimierte Entscheidungen, Innovation oder neue Einnahmequellen. Im Grunde genommen sind Daten nur dann „groß“, wenn sie letztlich einem Unternehmen einen Mehrwert einbringen.\u003C/li\u003E\n\u003C/ul\u003E\n\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_2060034519":{"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":"work",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-fa1881d729","additionalClasses":"headline-decoration","type":"heading2","lines":["Big Data im Vergleich zu herkömmlichen Daten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-09b95bcf9d","text":"\u003Cp\u003EEs gibt keinen einzigen Faktor, der darüber entscheidet, ob es sich um große oder traditionelle Daten handelt. Einige der grundlegenden Unterschiede umfassen den Wert, wie oben erwähnt, und ob er effektiv mit traditionellen oder älteren Tools analysiert werden kann. Traditionelle Daten sind ähnlich wie Datenbanken strukturiert und basieren auf statistischen Methoden und herkömmlichen Abfragetools wie SQL, um analysiert zu werden. Big Data entwickelt sich schnell und umfasst riesige Datasets in unterschiedlichen Formaten, darunter strukturierte, unstrukturierte und semistrukturierte Daten. Herkömmliche Datenanalysetools können die Skalierbarkeit oder Komplexität von Big Data nicht verarbeiten oder analysieren. Deshalb erfordert Big Data für die Analyse verteilte Systeme und fortschrittliche Tools wie maschinelles Lernen. \u003C/p\u003E\n\u003Cp\u003ETraditionelle Datenanalytik verarbeitet überschaubare Mengen an Informationen, wie z. B. die Ausführung eines tagesaktuellen Verkaufsberichts aus einer einzigen, strukturierten Finanzdatenbank, indem sie in vorhersehbaren Batches verarbeitet wird. Umgekehrt sind Big-Data-Analytics-Lösungen erforderlich, wenn es darum geht, riesige Mengen an Streaming-Daten zu verarbeiten, wie z. B. eine globale Ridesharing-App, die Millionen von Fahrzeugen überwacht. In dieser Situation müssen beispielsweise Daten mit hoher Geschwindigkeit (in Millisekunden) erfasst und verarbeitet werden, um geschätzte Echtzeit-Ankunftszeiten und dynamische Preise zu berechnen. \u003C/p\u003E\n\u003Cp\u003EAuch hier muss Big Data eine große Vielfalt bewältigen und integriert strukturierte GPS-Koordinaten mit unstrukturiertem Feedbacktext von Fahrern und Bildern. Ausgeklügelte Techniken sind erforderlich, um die Richtigkeit (Vertrauenswürdigkeit) zu verwalten und sicherzustellen, dass der ultimative unternehmerische Nutzen herausgeholt wird – eine Komplexität, für die herkömmliche Systeme einfach nicht entwickelt wurden.\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_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"container":"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":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"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":{"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":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2b27bdad52","additionalClasses":"headline-decoration","type":"heading2","lines":["Wie funktioniert Big Data Analytics?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2d2e09958d","text":"\u003Cp\u003EBig Data Analytics funktioniert über einen systematischen End-to-End-Workflow, der auf enorme Skalierbarkeit und Komplexität ausgelegt ist und Rohinformationen letztlich in verwertbare Erkenntnisse verwandelt. Um diesen Prozess zu veranschaulichen, verwenden wir das hypothetische Beispiel eines großen Kreditkartenunternehmens, das Echtzeit-Finanzbetrug verhindern will.\u003C/p\u003E\r\n\u003Cp\u003EDer Workflow für dieses Beispiel zeigt, wie Erkenntnisse aus der Big Data-Analytics kontinuierliche Streams von Transaktionsdaten in Prognosemodelle und sofortige Warnungen verwandeln, die in jeder Phase spezielle Cloud-Technologien erfordern.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDatenerfassung (Ingestion)\u003C/h3\u003E\r\n\u003Cp\u003EIn dieser Anfangsphase werden riesige Datenmengen aus verschiedenen Quellen erfasst. So erfasst das Kreditkartenunternehmen kontinuierlich Milliarden von Echtzeit-Ereignissen: jeden Kauf, jede Geldautomatenabhebung und jeden Online-Anmeldeversuch über Kartenzahlungen, E-Commerce-APIs und Anwendungsprotokolle weltweit.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDatenbereinigung und Vorverarbeitung\u003C/h3\u003E\r\n\u003Cp\u003ERohdaten sind oft unordentlich und erfordern sofortige Bereinigung, Standardisierung und Anreicherung, um die Genauigkeit zu gewährleisten. Das ist die Transformationsphase. Die \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-discovery/\"\u003EData Discovery\u003C/a\u003E ist ein inhärenter Bestandteil dieser Phasen, da sie das Erforschen und Verstehen der Daten vor oder während der Analyse beinhaltet. Duplikate, Fehler oder schwache Daten können irreführende Erkenntnisse liefern. Beim Beispiel Kreditkartenunternehmen werden Datenströme sofort auf fehlende Felder überprüft, standardisiert (z. B. um sicherzustellen, dass alle Zeitzonen einheitlich sind) und mit externen Daten wie bekannten Betrugslisten oder der Standorthistorie des Kunden angereichert.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDatenspeicherung und -verwaltung\u003C/h3\u003E\r\n\u003Cp\u003EDie riesigen, transformierten Datasets müssen in skalierbaren, flexiblen Architekturen gespeichert werden, die Rechen- und Speicherressourcen voneinander trennen. Die sauberen Daten des Kreditkartenunternehmens werden auf einer Cloud-Datenplattform gespeichert, die die Petabytes an Datensätzen verarbeitet, sodass verschiedene Analyseteams auf dieselbe Single Source of Truth zugreifen können, ohne die Performance zu beeinträchtigen.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDatenanalyse \u003C/h3\u003E\r\n\u003Cp\u003EHier werden fortschrittliche Techniken wie maschinelles Lernen und statistische Modellierung angewendet, um Muster zu erkennen und Ergebnisse vorherzusagen. So können beispielsweise Modelle für maschinelles Lernen Kaufhistorie, Standort und Ausgabengewohnheiten von Nutzenden in Echtzeit anhand einer Transaktion analysieren. Erkennt das Modell eine statistisch signifikante Anomalie, z. B. eine Karte, die innerhalb einer Stunde auf zwei Kontinenten verwendet wird, wird die Transaktion gekennzeichnet.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDatenvisualisierung und Reporting\u003C/h3\u003E\r\n\u003Cp\u003EIn der letzten Phase werden die komplexen Ergebnisse in Dashboards oder automatisierten Aktionen für geschäftliche Benutzer präsentiert. Bei dem hypothetischen Kreditkartenunternehmen werden sofort automatisierte Systeme ausgelöst, um die verdächtige Transaktion zu blockieren und eine Betrugsmeldung an den Kunden zu senden. Datenanalysten sehen sich Dashboards an, die aggregierte Trends von Betrugsversuchen über verschiedene Regionen und Kartentypen hinweg für die strategische Planung zeigen.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_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":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e9ed263f34","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum ist Big Data Analytics wichtig?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1842037b2f","text":"\u003Cp\u003EBig Data Analytics ist eine wichtige Komponente von Business Intelligence, da sie über retrospektives Reporting hinausgeht und sich auf prädiktive Einblicke und Analysen konzentriert. Durch diesen Mechanismus, der riesige Datenspeicher, selbst unstrukturierte Daten, in verwertbare Einblicke verwandelt, verschaffen sich Unternehmen einen enormen Wettbewerbsvorteil, indem sie alles vom Umsatz über die Effizienz bis hin zur Kundenerfahrung vorantreiben. \u003C/p\u003E\n\u003Cp\u003EDieser Prozess verbessert sofort die Entscheidungsfindung, indem Ratespiele durch Informationen ersetzt werden, die beantworten, was wahrscheinlich als Nächstes geschehen wird und wie am besten vorzugehen ist, wodurch sich ein Wettbewerbsvorteil verschafft wird.\u003C/p\u003E\n\u003Cp\u003EÜber Strategien hinaus wirkt sich Big Data direkt auf den Gewinn und die Kundenbeziehungen aus. Sie steigert die betriebliche Effizienz, indem sie Streaming-Daten von allen Maschinen und Systemen analysiert und so vorausschauende Wartungen ermöglicht und verschwenderische Ausgaben in globalen Workflows eliminiert. Gleichzeitig fördert sie ein tieferes Verständnis der Kundschaft, indem sie verschiedene Daten – von Transaktionen bis hin zur sozialen Stimmung – zusammenführt, um eine detaillierte 360-Grad-Ansicht zu erstellen, die hyperpersonalisierte Angebote fördert und die Kundentreue steigert. Die extrem schnelle Analyse von Daten dient auch als primäre Verteidigung für die Risikominderung, indem \u003Ca href=\"https://www.snowflake.com/de/solutions/departments/cybersecurity/\"\u003EBedrohungen\u003C/a\u003E wie Finanzbetrug und Cyberangriffe sofort in Echtzeit erkannt und neutralisiert werden.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"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":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4b2f1697dd","additionalClasses":"headline-decoration","type":"heading2","lines":["Big-Data-Analysemethoden"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e354eebbdf","text":"\u003Cp\u003EBig Data Analytics verfolgt einige wesentliche analytische Ansätze:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDeskriptive Analytik\u003C/h3\u003E\r\n\u003Cp\u003EDeskriptive Analytik ist die grundlegendste Analyse, bei der Daten verwendet werden, um die Frage zu beantworten, was in der Vergangenheit passiert ist. Dabei werden Daten aggregiert, gezählt und zusammengefasst, um einen Kontext zu vergangenen Ereignissen und Performance zu schaffen, wie z. B. Verkaufsdaten aus einem letzten Quartal. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDiagnostische Analytik\u003C/h3\u003E\r\n\u003Cp\u003ETechniken wie Data Mining und Kausalitätsanalyse zielen darauf ab, herauszufinden, „warum“ etwas passiert ist, um die Grundursache für ein bestimmtes Ergebnis zu bestimmen, wie eine bestimmte Kampagne, die zu Kunden-Leads oder einer verringerten Abwanderung geführt hat.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EPrädiktive Analytik\u003C/h3\u003E\r\n\u003Cp\u003EMithilfe statistischer Modelle und maschinellen Lernens nutzt diese Art der Analyse vergangene Muster, um künftige Wahrscheinlichkeiten zu prognostizieren und die Frage zu beantworten: "Was wird wahrscheinlich als Nächstes passieren?" Dies kann auf viele Branchen und Anwendungsfälle angewendet werden, z. B. auf die Prognose der zukünftigen Verkaufsnachfrage, die Prognose der Wartung von Fahrzeugflotten oder die Prognose, wann Studierende wahrscheinlich zurückfallen und vorausschauende Unterstützung benötigen. Prädiktive Analytik ist ein leistungsstarkes Werkzeug im \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003EMarketing\u003C/a\u003E, mit dem datengestützte Einblicke Kampagnen gestalten und Kunden gewinnen, halten und pflegen können. Und hier ermöglicht Big Data Analytics die Möglichkeiten von \u003Ca href=\"https://www.snowflake.com/de/product/snowflake-intelligence/\"\u003EML- und KI-Modellen\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EPräskriptive Analytik\u003C/h3\u003E\r\n\u003Cp\u003EDies ist eine der fortschrittlichsten Formen der Big Data Analytics, die über die Vorhersage, was passieren könnte, hinausgeht und vorgibt, was wir dagegen tun sollten. Oft werden Entscheidungen in Echtzeit mithilfe von Optimierung und Simulation getroffen, z. B. durch dynamische Anpassung der Preise, um den Gewinn anhand des aktuellen Bestands und der aktuellen Nachfrage zu maximieren.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"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":"equation",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-2e6ade4460","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile von Big Data Analytics"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-ceffc33241","text":"\u003Cp\u003EBig Data Analytics liefert einen messbaren Wert im gesamten Unternehmen, indem es eine Reihe kritischer, datengestützter Vorteile ermöglicht, die sich direkt auf Rentabilität, Strategie und Risiko auswirken. Zu den Vorteilen gehören:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EKostensenkung und betriebliche Effizienz\u003C/h3\u003E\n\u003Cp\u003EDurch die Analyse von Streaming-Daten aus Lieferketten und Sensoren können Unternehmen Verschwendung erkennen und beseitigen. Das ermöglicht eine präzise Ressourcenzuweisung und eine vorausschauende Wartung, um kostspielige Geräteausfallzeiten zu verhindern. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVerbesserte Kundeneinblicke\u003C/h3\u003E\n\u003Cp\u003EDurch die Integration und Analyse von Transaktionen neben unstrukturierten Daten wie Social-Media-Stimmung erhalten Unternehmen eine detaillierte 360-Grad-Sicht auf die Verbraucher:innen und gehen weit über das grundlegende demografische Verständnis hinaus.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESchnellere Entscheidungsfindung\u003C/h3\u003E\n\u003Cp\u003EBig Data versetzt Führungskräfte in die Lage, schnell an Rätselraten vorbeizugehen und liefert zuverlässige, datengestützte Informationen, die nicht nur künftige Ergebnisse prognostizieren, sondern auch die beste Vorgehensweise vorschlagen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVerbesserte Produktentwicklung\u003C/h3\u003E\n\u003Cp\u003EAnalysen decken verborgene Verhaltensmuster und Vorlieben von Kund:innen auf und führen Produktteams an, bestehende Angebote zu optimieren oder schnell innovative neue Funktionen zu entwickeln, die direkt auf die Marktnachfrage eingehen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EWettbewerbsvorteil\u003C/h3\u003E\n\u003Cp\u003EBig Data Analytics ermöglicht es Unternehmen, ihre Marktposition erheblich zu verbessern, indem sie tiefgreifende, zeitnahe Einblicke in Strategie und Innovation einfließen lassen. Das führt direkt zu mehr Umsatz, Kundenzufriedenheit und mehr. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERisikomanagement und Betrugserkennung\u003C/h3\u003E\n\u003Cp\u003EDie Fähigkeit, Daten mit extremer Geschwindigkeit zu verarbeiten, ist für eine moderne Verteidigung unerlässlich. So können Finanzinstitute und Sicherheitsteams anomale Transaktionen oder Cyberangriffe sofort in Echtzeit erkennen und neutralisieren.\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_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"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":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2e9313a67d","additionalClasses":"headline-decoration","type":"heading2","lines":["Herausforderungen der Big Data Analytics"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4d08b13e61","text":"\u003Cp\u003ETrotz des transformativen Potenzials von Big Data Analytics stehen Unternehmen bei der effektiven Nutzung dieser Informationen vor erheblichen Herausforderungen, vor allem aufgrund der schieren Skalierbarkeit und Komplexität, die mit den fünf V verbunden sind. Zu diesen Schwierigkeiten gehören technische, Sicherheits- und Talentbereiche, die moderne, integrierte Lösungen erfordern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDatenvolumen\u003C/h3\u003E\n\u003Cp\u003EDas einfache Speichern und Verarbeiten von Petabytes an Informationen ist kostspielig, und die herkömmliche Infrastruktur versagt oft bei dem riesigen Umfang an Daten, die täglich generiert werden.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDatenvielfalt\u003C/h3\u003E\n\u003Cp\u003EDie Notwendigkeit, viele verschiedene Datenformate zu integrieren und zu vereinheitlichen – von strukturierten Datenbanken bis hin zu unstrukturierten Videos und Texten –, schafft komplexe und arbeitsintensive \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003EPipelines\u003C/a\u003E für Daten.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDatengeschwindigkeit\u003C/h3\u003E\n\u003Cp\u003EUm kontinuierliche, schnelle Streams eingehender Daten in Echtzeit zu verarbeiten, sind spezielle Streaming-Technologien erforderlich, die effizient entwickelt und gewartet werden müssen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDatenqualität und -richtigkeit\u003C/h3\u003E\n\u003Cp\u003EDa Daten aus zahlreichen Quellen mit unterschiedlicher Zuverlässigkeit stammen, bleibt die Gewährleistung der Genauigkeit, Einheitlichkeit und Vertrauenswürdigkeit der Informationen eine grundlegende Hürde.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESicherheit und Datenschutz\u003C/h3\u003E\n\u003Cp\u003EDer Schutz riesiger, verteilter Datasets, die sensible Kundeninformationen enthalten, bringt komplexe Compliance-Risiken und Governance-Herausforderungen mit sich, die verschiedenen globalen Vorschriften unterliegen können.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESkalierbarkeit und Performance \u003C/h3\u003E\n\u003Cp\u003EUnternehmen stehen vor der Herausforderung, sicherzustellen, dass ihre Datenplattformen sofort skaliert werden können, um schwankenden Anforderungen gerecht zu werden, ohne dabei die Abfragegeschwindigkeit zu beeinträchtigen oder übermäßige Betriebskosten zu verursachen.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ETalent und Fachwissen\u003C/h3\u003E\n\u003Cp\u003EBig-Data-Analysemethoden erfordern qualifizierte Data Scientists und Data Engineers, die über das spezifische Fachwissen verfügen, um komplexe Big-Data-Technologien zu verwalten, zu optimieren und daraus Nutzen zu 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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"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":"types",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-18ea648e8f","additionalClasses":"headline-decoration","type":"heading2","lines":["Anwendungsfälle für Big Data Analytics"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-3bc07f124d","text":"\u003Cp\u003EWenn Big Data Analytics auf modernen Cloud-Datenplattformen basiert, entstehen individuelle, hochwertige Anwendungsfälle, die auf die spezifischen Datenherausforderungen jeder großen Branche zugeschnitten sind. Diese Anwendungsfälle umfassen:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGesundheitswesen\u003C/h3\u003E\n\u003Cp\u003EDurch die Zusammenführung unstrukturierter Patientenakten, Daten aus klinischen Studien und Informationen zu Leistungsansprüchen können Leistungserbringer eine 360-Grad-Ansicht eines Patienten erstellen, um Versorgungspfade zu personalisieren und die allgemeinen Gesundheitsergebnisse zu verbessern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EFinanzen\u003C/h3\u003E\n\u003Cp\u003EFinanzinstitute nutzen die Hochgeschwindigkeitsverarbeitung von Big Data Analytics, um Milliarden von Transaktionen in Echtzeit zu analysieren. Das ermöglicht eine sofortige Betrugserkennung, prädiktive Analytik und ausgefeilte algorithmische Handelsstrategien.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHandel\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-for-retailers/\"\u003EHändler\u003C/a\u003E kombinieren Online-Clickstream-Daten, Aufzeichnungen über Treueprogramme und Lagerbestände, um die Nachfrage genau vorherzusagen und hyperpersonalisierte Produktempfehlungen zu geben, die den Umsatz steigern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EFertigung\u003C/h3\u003E\n\u003Cp\u003EDie Integration riesiger Mengen an IT- und Betriebstechnologie-Sensordaten aus der \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-manufacturing/\"\u003EFertigungshalle\u003C/a\u003E ermöglicht vorausschauende Wartung und trägt zur Optimierung von Produktionsprozessen für mehr betriebliche Effizienz bei.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ETransport und Logistik\u003C/h3\u003E\n\u003Cp\u003EUnternehmen analysieren Echtzeit-GPS- und Wetterdaten sowie historische Trends, um komplexe Lieferrouten zu optimieren, den Flottenbetrieb proaktiv zu steuern und die Resilienz der Lieferkette zu steigern.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBildungswesen\u003C/h3\u003E\n\u003Cp\u003EUniversitäten zentralisieren Studenten-, Lehrkörper- und Verwaltungsakten, um eine 360-Grad-Ansicht zu erstellen, mit der sie gefährdete Studierende identifizieren und Lerninterventionen personalisieren können, um die Bindung zu stärken. Analysen helfen auch, Verschwendung bei der Gebäudenutzung zu reduzieren und unterstützen Alumni-Beziehungen und die Mittelbeschaffung. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EUnterhaltung\u003C/h3\u003E\n\u003Cp\u003EMedienunternehmen analysieren große Mengen an Sehgewohnheiten und Daten zur sozialen Stimmung, um eine Content-Erstellungsstrategie zu entwickeln und Inhaltsempfehlungen in Echtzeit zu optimieren. So wird das Engagement und die Bindung von Abonnent:innen maximiert.\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_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container",":items":{"title_v2_copy_copy":{"id":"title-v2-2055c75c25","additionalClasses":"headline-decoration","type":"heading2","lines":["Tools für Big Data Analytics"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-e83d8b0097","text":"\u003Cp\u003EAufgrund der enormen Skalierbarkeit und Komplexität moderner Daten reichen relationale Single-Server-Tools nicht mehr aus. Big Data Analytics erfordert ein leistungsstarkes Ökosystem spezialisierter Technologien. Diese Tools sind oft nach Funktionen kategorisiert: Speicherung, Verarbeitung und Analyse. Hier einige beliebte Big-Data-Analysetools:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EHadoop\u003C/h3\u003E\n\u003Cp\u003EApache Hadoop ist ein grundlegendes Open-Source-Framework, das entwickelt wurde, um riesige Datasets zu verwalten und zu verarbeiten, indem der Workload auf ein Netzwerk von Standardservern verteilt wird. Sie bietet die grundlegenden Funktionen für fehlertolerante Speicherung und Stapelverarbeitung im Petabyte-Bereich.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESpark\u003C/h3\u003E\n\u003Cp\u003EApache Spark ist eine fortschrittliche, mehrsprachige Verarbeitungs-Engine, die auf Geschwindigkeit ausgelegt ist. Es beschleunigt analytische Workloads, insbesondere maschinelles Lernen, indem die Daten im gesamten Cluster gespeichert bleiben, was zu einer überragenden Performance gegenüber plattenbasierten Systemen führt.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EStream-Analytics-Tools\u003C/h3\u003E\n\u003Cp\u003EDiese spezielle Software wird verwendet, um Daten sofort nach ihrer Generierung zu erfassen und zu analysieren – ein „Data in Motion“-Ansatz. Dies ist entscheidend für Anwendungsfälle, die sofortige Einblicke erfordern, wie z. B. die Überwachung von Internet of Things (IoT)-Sensoren oder die Erkennung von Finanzbetrug innerhalb von Millisekunden.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVerteilter Speicher\u003C/h3\u003E\n\u003Cp\u003EDiese Architekturen sind darauf ausgelegt, riesige Dateien in Blöcke aufzuteilen und sie sicher auf zahlreiche physische oder virtuelle Maschinen zu verteilen. 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