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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});",":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":"container-79aca8904f",":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-v2-7d69338996","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-93cea212a9","text":"\u003Cp\u003EUnternehmen und Organisationen generieren täglich riesige Datenmengen, doch in ihrem rohen Zustand bergen sie mehr Versprechen als tatsächlichen Wert. Wenn Sie ein Rezept machen oder ein Produkt auf einem Fließband entwickeln, entsteht am Ende eine Summe seiner Teile, wobei alles zusammenkommt, um etwas Nützliches (oder Leckeres) zu kreieren. Und wenn bei Unternehmen ein Durcheinander an Rohdaten vorliegt, müssen sie erst einmal alles verstehen, bevor sie für irgendwen nützlich sein können. Dies geschieht durch eine Reihe von Schritten, die als Datenverarbeitung bezeichnet werden.\u003C/p\u003E\n\u003Cp\u003EDatenverarbeitung beschreibt die Strukturierung von Rohdaten in einem nützlichen Format. Durch eine Reihe von Operationen decken Unternehmen den verborgenen Wert auf, der in Zahlenspalten, Seiten mit Umfrageantworten und Tabellen voller Informationen verborgen ist. Sie bildet das Kernstück der Geschäftsstrategie und macht alles möglich, von Business Analytics bis hin zu maschinellem Lernen (ML).\u003C/p\u003E\n\u003Cp\u003EIn diesem Leitfaden untersuchen wir, was Datenverarbeitung ist und warum sie wichtig ist, die Phasen der Datenverarbeitung, Arten der Datenverarbeitung, Datenverarbeitungsmethoden sowie Datenverarbeitungstools und -technologien. Abschließend stellen wir Ihnen einige der häufigsten Fragen zu diesem geschäftskritischen Vorgang.\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-v2-690c1ec1c4","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Datenverarbeitung?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-02a39c20a3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDatenverarbeitung ist eine systematische Vorgangsreihe, die unorganisierte Rohdaten in verwertbare Informationen umwandelt, aus denen Unternehmen aussagekräftige Erkenntnisse gewinnen und fundierte Entscheidungen treffen können. Sie ist ein grundlegender Bestandteil der Geschäftsstrategie und entscheidend, um Datenanalysen zu ermöglichen.\u003C/p\u003E\n\u003Cp\u003EIn der Vergangenheit war die Datenverarbeitung ein sehr mühseliger, zeitaufwendiger manueller Prozess. Menschliche Computer – Menschen, die die \u003Ca href=\"https://www.nasa.gov/centers-and-facilities/jpl/when-computers-were-human/\"\u003EBerufsbezeichnung „Computer“\u003C/a\u003E erhielten – nutzten physische Tools wie Bücher, Formulare und Taschenrechner sowie papierbasierte Systeme, um Daten zu erfassen, zu speichern und zu analysieren. Es dauerte \u003Ca href=\"https://www.rudderstack.com/learn/data-collection/data-collection-history/\"\u003Eberüchtigterweise\u003C/a\u003E \u003Ci\u003Esieben Jahre\u003C/i\u003E, bis die Vereinigten Staaten die Ergebnisse der Volkszählung von 1880 veröffentlichten, da die manuellen Ermittlungsprozesse langsam waren, was Herman Hollerith, einen Angestellten des U.S. Census Bureau, dazu veranlasste, die Tabuliermaschine zu erfinden. Sie reduzierte die Zeit für die Verarbeitung von Volkszählungsdaten drastisch von Jahren auf Monate und legte den Grundstein für die moderne Datenverarbeitungsbranche.\u003C/p\u003E\n\u003Cp\u003EHeute ist Datenverarbeitung ein elektronischer Prozess, der von Computern und Automatisierung verwaltet wird und in der Regel von Datenanalysten, Datenverarbeitern, \u003Ca href=\"https://www.snowflake.com/de/product/data-engineering/\"\u003EData Engineers\u003C/a\u003E und Data Scientists durchgeführt wird. \u003Ca href=\"https://www.snowflake.com/de/product/ai/\"\u003EKI und ML\u003C/a\u003E spielen bei der Bearbeitung besonders großer Datasets eine wichtige Rolle. Datenverarbeitung wird oft als in einem Zyklus stattfindend beschrieben. Es werden eine Reihe von Maßnahmen ergriffen, damit Daten aus ihrem Rohzustand analysiert, interpretiert und dann gespeichert werden.\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_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__714116681":"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":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-da675d27d1","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum ist Datenverarbeitung so wichtig?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0b000c8baf","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EOhne Datenverarbeitung wären die riesigen Datenmengen, die Unternehmen sekundengenau generieren, nichts anderes als digitales Rauschen. Datenverarbeitung schließt die Kluft zwischen unverarbeiteten Informationen, die in ihrem Rohzustand selten nützlich sind, und wichtigen Erkenntnissen, die Geschäftsentscheidungen treffen und Unternehmen einen Wettbewerbsvorteil verschaffen können.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EBessere Entscheidungsfindung: \u003C/b\u003EUnternehmen können sich nicht auf Annahmen und Vermutungen verlassen, wenn sie mithalten und wachsen wollen. Die durch die Datenverarbeitung gewonnenen klaren Erkenntnisse können die Entscheidungsfindung auf verschiedene Weise verbessern, darunter:\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMarkttrends erkennen:\u003C/b\u003E Vertriebsdaten können Auskunft darüber geben, welche Ihrer Produkte sich gut verkaufen, welche Demografie sie kaufen, zu welcher Jahreszeit sie sich gut verkaufen usw.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EBetriebliche Effizienz steigern\u003C/b\u003E: Die Analyse von Lieferketten-, Logistik- und Produktionsdaten kann Unternehmen dabei helfen, Abfall und Engpässe zu erkennen und ihre Prozesse zu optimieren.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDatengestützte Prognosen treffen:\u003C/b\u003E Prädiktive Analytik nutzt historische Daten, um Ergebnisse zu prognostizieren. So können Unternehmen Kundenbedürfnisse antizipieren, Bestände verwalten und Risiken minimieren.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003EGesteigerte Genauigkeit und Zuverlässigkeit:\u003C/b\u003E Unverarbeitete Daten enthalten sehr oft Fehler, Duplikate und Uneinheitlichkeiten. Und in vielen Branchen wie \u003Ca href=\"https://www.snowflake.com/de/fundamentals/governance-risk-compliance/\"\u003EGovernance, Risiko und Compliance\u003C/a\u003E, Betrugserkennung und Finanzen kann ein einziger Fehler oder eine Unstimmigkeit zu noch größeren Komplikationen führen. Der Datenbereinigungsschritt der Datenverarbeitung (auf den wir in Kürze eingehen) identifiziert und korrigiert diese Probleme, wodurch die Daten genauer und zuverlässiger werden, wenn die Analyse ansteht.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EGrößerer Wettbewerbsvorteil:\u003C/b\u003E Die effektive Verarbeitung und Nutzung von Daten ist ein wichtiges Unterscheidungsmerkmal für Unternehmen, die sich einen Wettbewerbsvorteil verschaffen wollen. Hier einige Vorteile:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPersonalisierte Kundenerfahrungen:\u003C/b\u003E Indem Unternehmen Kundendaten verarbeiten, können sie Kund:innen personalisierte Empfehlungen, gezieltes Marketing und Services anbieten, an denen sie interessiert sein könnten. Das steigert die Markentreue und Kundenbindung.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EReaktion auf Marktveränderungen:\u003C/b\u003E Mit der Echtzeit-Datenverarbeitung können Unternehmen schnell auf Marktveränderungen reagieren – egal, ob es sich um einen Produktrückgang eines Mitbewerbers oder eine veränderte Kundennachfrage handelt.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003EVerstärkte Datensicherheit und Compliance:\u003C/b\u003E Bei Datenverarbeitung geht es nicht nur darum, Daten nützlich zu machen – es geht auch darum, sie zu schützen. \u003Ca href=\"https://www.acceldata.io/blog/data-compliance\"\u003ESpezielle Schutzmaßnahmen\u003C/a\u003E werden in die Datenbereinigung und -organisation integriert, darunter \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003EDatenmaskierung\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003EAnonymisierung\u003C/a\u003E, Verschlüsselung und Tokenisierung. Datenverarbeitungssysteme setzen außerdem Regeln durch, wer auf Daten zugreifen, sie ändern oder löschen kann. Darüber hinaus enthalten viele Datenschutzgesetze wie die DSGVO und HIPAA strenge Anforderungen darüber, welche Daten erfasst, wie sie verwendet werden dürfen usw. Datenverarbeitungssysteme dokumentieren sorgfältig jeden Schritt des Datenlebenszyklus, von der Erfassung bis zur Löschung. So entsteht ein Auditpfad, der belegt, dass ein Unternehmen Vorschriften einhält.\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_":{"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-a3eb1fd263","additionalClasses":"headline-decoration","type":"heading2","lines":["Die Phasen der Datenverarbeitung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8bd246ea5a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVorhin haben wir in diesem Leitfaden die Datenverarbeitung mit einer Fertigungslinie verglichen. Ähnlich wie die Phasen beim Bau eines Autos, vom Aushämmern des Chassis bis zum finalen Lackieren und Polieren, folgt die Datenverarbeitung einem strukturierten, mehrstufigen Workflow. Jeder dieser Schritte ist entscheidend, um unübersichtliche Rohdaten in saubere und zuverlässige Daten zu verwandeln, auf die Unternehmen bei fundierten Entscheidungen und der Entwicklung solider Strategien angewiesen sind.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Sammlung\u003C/h4\u003E\n\u003Cp\u003EEs ist an der Zeit, diese Daten zu sammeln, und sie können aus zahlreichen Quellen stammen: Transaktionsprotokolle und Unternehmensdatenbanken, Statistiken über Social-Media-Engagement und Kundenumfragen. Oft sind sie in \u003Ca href=\"https://www.snowflake.com/de/product/analytics/\"\u003EData Lakes und Warehouses\u003C/a\u003E untergebracht. Entscheidend ist, dass die Daten, die in diesem ersten Schritt extrahiert werden, relevant und genau sind und aus zuverlässigen Quellen stammen. Andernfalls laufen sie Gefahr, die Endergebnisse zu verzerren und das Projekt von Anfang an vollständig zu gefährden.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Datenaufbereitung\u003C/h4\u003E\n\u003Cp\u003EOft als Vorverarbeitung bezeichnet, ist dies die kritischste und zeitaufwendigste Phase, in der Daten bereinigt und organisiert werden, um Qualität und Einheitlichkeit zu gewährleisten. Diese Schritte umfassen:\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EDatenbereinigung: \u003C/b\u003EKorrektur von Fehlern, Ausfüllen fehlender Werte, Entfernen doppelter oder irrelevanter Daten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003EDatentransformation\u003C/a\u003E:\u003C/b\u003E Umwandlung von Daten in ein einheitliches Format (Standardisierung des Datumsformats, Änderung von Text in numerischen Code usw.).\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDatenvalidierung:\u003C/b\u003E Überprüfung der Daten anhand von Regeln, um ihre Genauigkeit zu gewährleisten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.fullstory.com/blog/data-enrichment/\"\u003EDatenanreicherung\u003C/a\u003E: \u003C/b\u003EErweiterung des Datasets um zusätzliche relevante Informationen aus externen Quellen.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E\u003Cbr\u003E\n3. Eingabe\u003C/h4\u003E\n\u003Cp\u003EHier werden aufbereitete Daten in das Verarbeitungssystem eingespeist und es ist die erste Stufe, in der Rohdaten beginnen, die Form von nutzbaren Daten anzunehmen. Beispiele für Verarbeitungssysteme können Software oder ein Algorithmus sein, der für bestimmte Datentypen oder Analyseziele entwickelt wurde, wie \u003Ca href=\"https://www.ibm.com/think/topics/apache-spark\"\u003EApache Spark\u003C/a\u003E für große Datasets. Manuelle Dateneingabe (für kleine Datasets), Datenimport aus externen Quellen oder automatische Datenerfassung sind allesamt Möglichkeiten, um Daten in dieser Phase in diese Systeme einzugeben.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. Verarbeitung\u003C/h4\u003E\n\u003Cp\u003EWie der Name schon sagt, ist dies der Kern des Datenverarbeitungszyklus. Es gibt einige verschiedene Techniken, um die Daten in aussagekräftige Informationen zu verwandeln, je nach gewünschtem Ergebnis oder den Erkenntnissen, die aus den Daten gewonnen werden müssen. Dazu gehören:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESortierung: \u003C/b\u003EAnordnung von Daten in einer bestimmten Reihenfolge.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFilterung:\u003C/b\u003E Auswahl bestimmter Teilmengen von Daten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBerechnung: \u003C/b\u003EDurchführung mathematischer Operationen, wie z. B. Berechnung von Summen oder Durchschnitten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAggregation:\u003C/b\u003E Zusammenfassung von Daten aus mehreren Datensätzen.\u003Cbr\u003E\n \u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E5. Output und Interpretation\u003C/h4\u003E\n\u003Cp\u003ENach der Verarbeitung werden die Daten in einem überschaubaren und leicht verständlichen Format präsentiert. Die Ausgabe ist das Endprodukt, das eine Grafik, ein Dashboard oder eine andere visuelle Darstellung sein kann. Die Interpretationsphase ist die Analyse des Outputs, um daraus Schlüsse zu ziehen, Trends zu erkennen und fundierte Entscheidungen zu treffen – und hier wird der Wert der verarbeiteten Daten endlich realisiert.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E6. Speicher\u003C/h4\u003E\n\u003Cp\u003EDer letzte Schritt besteht darin, die verarbeiteten Daten sicher in Datenbanken oder Data Warehouses zu speichern, um sie für die Zukunft nutzen und abrufen zu können. Dieser Schritt ist aus mehreren Gründen entscheidend:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EAudit und Compliance:\u003C/b\u003E Sie schafft Aufzeichnungen für rechtliche und regulatorische Zwecke.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EZukunftsanalyse:\u003C/b\u003E Die Daten können als Grundlage für weitere, komplexere Analysen dienen.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EReferenz:\u003C/b\u003E Sie bietet eine zuverlässige Quelle historischer Informationen für die Entscheidungsfindung.\u003C/li\u003E\n\u003C/ul\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":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7d1df30059","additionalClasses":"headline-decoration","type":"heading2","lines":["Arten der Datenverarbeitung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2b156b890f","text":"\u003Cp\u003EVerschiedene Methoden werden eingesetzt, um Rohdaten in aussagekräftige, verwertbare Informationen zu verwandeln. Zwar gibt es viele, und jedes ist für unterschiedliche Szenarien und Anforderungen am besten geeignet, doch Stapelverarbeitung, Echtzeitverarbeitung und Onlineverarbeitung sind drei der häufigsten.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Batch-Verarbeitung\u003C/h4\u003E\n\u003Cp\u003EBei der \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/streaming-data-pipelines/\"\u003EBatch-Verarbeitung\u003C/a\u003E handelt es sich um ein Verfahren, bei dem über einen längeren Zeitraum eine große Datenmenge erfasst und dann in einem Stapel auf einmal verarbeitet wird. Dieser Ansatz eignet sich ideal für Aufgaben, die nicht zeitkritisch sind und außerhalb der Spitzenzeiten geplant werden können, um Rechenressourcen zu sparen. Ideale Anwendungsfälle können Abrechnungssysteme, monatliche Abrechnungen, Tagesabschlussberichte und die Erstellung von Kontoauszügen sein. Ein Kreditkartenunternehmen kann beispielsweise alle Transaktionen über den Tag hinweg erfassen und über Nacht in einem einzigen Batch verarbeiten, um Kundenkonten zu aktualisieren.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Echtzeitverarbeitung\u003C/h4\u003E\n\u003Cp\u003EDie Echtzeitverarbeitung verarbeitet Daten sofort bei ihrer Entstehung und liefert sofortige Ergebnisse. Diese Methode ist entscheidend für Situationen, in denen die Umstellung von der Dateneingabe auf die Ausgabe sofort erfolgen muss, insbesondere für Systeme, in denen eine Verzögerung schwerwiegende Folgen haben könnte. Beispiele für diese Art der Datenverarbeitung sind die \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003EBetrugserkennung bei Finanztransaktionen\u003C/a\u003E, GPS-Systemen oder Flugsicherungssystemen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Online-Verarbeitung\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003EOnline-Verarbeitung\u003C/a\u003E ist eine interaktive Echtzeit-Verarbeitung. Es verarbeitet von Nutzenden initiierte Transaktionen, sobald sie auftreten, und gibt eine sofortige Reaktion. Das erleben Sie jeden Tag, wenn Sie mit Websites und Apps interagieren. Kurz gesagt: Ein Benutzer löst eine Anfrage aus oder gibt Daten ein, die das System sofort verarbeitet und Feedback gibt. Diese Systeme sind immer online und stehen jederzeit bereit, um Nutzeranfragen zu bearbeiten. E-Commerce, Online-Banking, Airline-Reservierungen und Online-Gaming nutzen die Online-Verarbeitung. Haben Sie schon einmal Konzert- oder Kinokarten online gekauft? So wird Ihre Zahlung verarbeitet und das System wird sofort aktualisiert, damit niemand sonst ein Ticket für dieselben Sitzplätze kaufen kann.\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__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-f9dd168e92","additionalClasses":"headline-decoration","type":"heading2","lines":["Methoden zur Datenverarbeitung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-ec3921a66e","text":"\u003Cp\u003EEs gibt verschiedene Methoden zur Verarbeitung von Daten, und nicht alle Methoden sind mit allen Verarbeitungstypen kompatibel.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Manuelle Datenverarbeitung\u003C/h4\u003E\n\u003Cp\u003EDabei handelt es sich um die älteste und traditionellste Datenverarbeitungsmethode, bei der Daten vollständig von Hand und ohne Zuhilfenahme von Maschinen erfasst, organisiert und analysiert werden. Sie ist langsam, arbeitsintensiv, fehleranfällig und nicht ideal für große Datenmengen. Aber es ist eine gute Wahl für kleine Betriebe oder Unternehmen oder wenn menschliches Urteilsvermögen unerlässlich ist, wie z. B. die Durchführung einer Neuauszählung der Stimmzettel während einer Wahl.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Mechanische Datenverarbeitung\u003C/h4\u003E\n\u003Cp\u003EWenn Sie einfache Maschinen und Geräte zur Datenverarbeitung verwenden, wie z. B. Taschenrechner, Schreibmaschinen oder Lochkartenmaschinen, verwenden Sie die mechanische Datenverarbeitung. Die Hollerith-Tabellenmaschine, die wir bereits in diesem Leitfaden erwähnt haben, ist ein Beispiel für diese Methode. Mechanische Datenverarbeitung eignet sich ideal für einfache Datenverarbeitungsaufgaben und liefert weniger Fehler als manuelle Datenverarbeitung, ist aber dennoch keine gute Wahl für große Datasets.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Elektronische Datenverarbeitung\u003C/h4\u003E\n\u003Cp\u003EDie \u003Ca href=\"https://www.lenovo.com/us/en/glossary/what-is-edp/?orgRef=https%253A%252F%252Fwww.google.com%252F&srsltid=AfmBOooBYFjcgNsJVG7eEUuzaIdrjkhzOJ_4Z3st5mhFK4tMT58CPgpa\"\u003Eelektronische Datenverarbeitung (EDV)\u003C/a\u003E ist die modernste und am weitesten verbreitete Methode, die auf elektronischen Lösungen wie Computern, Servern und Automatisierung basiert, um Daten zu verarbeiten. Es ist ein äußerst effizienter, präziser und skalierbarer Ansatz, der riesige Datenmengen in Echtzeit verarbeiten kann. EDP automatisiert den gesamten Datenverarbeitungszyklus, von der Eingabe bis zur Ausgabe und kommt heute in praktisch jeder Branche für alles zum Einsatz, vom einfachen Abrechnungssystem bis hin zu Big-Data-Anwendungen.\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_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__714116681":{"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":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-05352c53bf","additionalClasses":"headline-decoration","type":"heading2","lines":["Tools und Technologien zur Datenverarbeitung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-da5fbc5bd0","text":"\u003Cp\u003EModerne Datenverarbeitung basiert auf einer Kombination aus leistungsstarken Tools und aufkommenden Technologien, um wertvolle Erkenntnisse aus unverarbeiteten Rohdaten zu gewinnen. Diese Lösungen ermöglichen alles, vom einfachen Datenspeicher bis hin zu komplexen, automatisierten Analysen. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Datenbanken und Data Warehouses\u003C/h4\u003E\n\u003Cp\u003ESie sind grundlegende Tools für die Datenspeicherung und -verwaltung, haben aber in der \u003Ca href=\"https://www.snowflake.com/de/fundamentals/modernizing-data-pipelines/\"\u003EVerarbeitungs-Pipeline\u003C/a\u003E \u003Ca href=\"https://www.coursera.org/articles/data-warehouse-vs-database\"\u003Eunterschiedliche Zwecke\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EDatenbanken\u003C/b\u003E dienen dazu, Informationen aus einer einzigen Datenquelle für eine bestimmte Funktion Ihres Unternehmens zu speichern und zu organisieren. Stellen Sie sich das Ganze als sorgfältig organisierten Aktenschrank für einen einzigen Zweck vor. Sie sind auf schnelle, häufige Aufgaben und kleine Abfragen ausgelegt. Beliebte Datenbanken umfassen SQL-basierte Systeme wie MySQL, PostgreSQL und Microsoft SQL Server.\u003C/p\u003E\n\u003Cp\u003EUmgekehrt sind \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003EData Warehouses\u003C/a\u003E große, zentralisierte Repositorys, in denen riesige Mengen historischer Daten aus verschiedenen Quellen gespeichert werden können. Sie dienen der Analyse und sind im Grunde die Bibliothek, in der Datenanalysten Informationen finden, um Fragen zu komplexen Geschäftstrends zu beantworten. Sie wurden für die Ausführung komplexer Abfragen großer Datasets entwickelt, um Berichte und Business Intelligence zu generieren. \u003Ca href=\"https://www.snowflake.com/de/product/platform/\"\u003EData Warehouses\u003C/a\u003E nutzen oft Big-Data-Technologien wie Snowflake, Hadoop, Apache Spark und \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-lakes-and-data-lake-architecture/\"\u003EData Lakes\u003C/a\u003E. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Künstliche Intelligenz und maschinelles Lernen\u003C/h4\u003E\n\u003Cp\u003EKI und ML sind leistungsstarke Technologien, die jede Phase der Datenverarbeitung automatisieren und verbessern. Sie gehen über einfache Berechnungen hinaus, um Muster aufzudecken und Vorhersagen zu treffen. KI kann die Datenbereinigung und Datenaufbereitung automatisieren und Fehler automatisch erkennen und korrigieren, fehlende Werte ausfüllen und Datenformate standardisieren. Wenn ML-Modelle mit historischen Daten trainiert werden, können sie Prognosen treffen, Anomalien finden und Daten segmentieren.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Plattformen für Cloudtechnologie und Datenanalytik\u003C/h4\u003E\n\u003Cp\u003ECloud-Anbieter wie Amazon Web Services (AWS), Google Cloud Platform (GCP) und Microsoft Azure ermöglichen es Unternehmen, ihre Datenverarbeitungsressourcen nach Bedarf hoch- oder herunterzuskalieren, ohne teure On-Premise-Hardware kaufen oder warten zu müssen. Darüber hinaus ist es möglich, Big Data in großem Umfang zu verarbeiten, was für die meisten Unternehmen sonst unmöglich wäre.\u003C/p\u003E\n\u003Cp\u003EDatenanalyseplattformen sind Softwarelösungen, die oft cloudbasiert sind und eine vollständige Umgebung für die Datenverarbeitung bieten. Snowflake und Tableau bieten eine einheitliche Plattform, um Daten zu speichern, analytische Abfragen auszuführen, Visualisierungen zu erstellen und komplexe Workflows zu vereinfachen. Mit der \u003Ca href=\"https://www.snowflake.com/de/product/platform/\"\u003ESnowflake AI Data Cloud\u003C/a\u003E werden Daten beispielsweise für leistungsstarke Datenoperationen optimiert, sobald sie auf der Plattform geladen werden, und können auf großen Public Clouds ausgeführt 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_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-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":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-d2095dca3c","additionalClasses":"headline-decoration","type":"heading2","lines":["Fazit"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a6b017b749","text":"\u003Cp\u003EDatenverarbeitung ist die unverzichtbare Engine, die die Umwandlung von unorganisierten Rohdaten in geschäftskritische Erkenntnisse ermöglicht, die Unternehmen für fundierte Entscheidungen benötigen. Wir haben die Zeit der rein manuellen Protokollierung und Analyse von Daten weit hinter uns gelassen und verfügen nun über leistungsstarke, automatisierte Lösungen, die auf KI und ML basieren, um das schiere Datenvolumen zu bewältigen, das Unternehmen heute produzieren und das exponentiell wächst. Effiziente und intelligente Datenverarbeitung ist wichtiger denn je, um das Datenmeer zu verstehen, das Unternehmen täglich produzieren, um ihr künftiges Wachstum und ihren Erfolg zu sichern.\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__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":"faq",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-38afe450fc","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ zur Datenverarbeitung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-628f3213ca","showDivider":false,"accordionItemsList":[{"title":"Welche Arten von Software stehen für die Big Data-Verarbeitung zur Verfügung?","richText":"\u003Cp\u003E\u003Cb\u003EFrameworks für verteiltes Rechnen:\u003C/b\u003E Apache Hadoop, Apache Spark\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ECloudbasierte Data Warehouses:\u003C/b\u003E Google BigQuery, Amazon Redshift, Microsoft Azure HDInsight\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ENoSQL-Datenbanken:\u003C/b\u003E MongoDB, Apache Cassandra\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EStream-Verarbeitungssysteme: \u003C/b\u003EApache Flink, Apache Storm\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETools für Business Intelligence (BI) und Visualisierung: \u003C/b\u003ETableau, Microsoft Power BI\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EIntegrierte Datenplattformen:\u003C/b\u003E Snowflake\u003C/p\u003E\n"},{"title":"Welche Datenquellen werden für Big Data häufig verwendet?","richText":"\u003Cp\u003EBig Data kommt aus einer Vielzahl von Quellen, die sich grob in drei Arten einteilen lassen: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Estrukturierte, unstrukturierte und semistrukturierte Daten\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EStrukturiert:\u003C/b\u003E Stark organisiert, folgt einem vorgegebenen Format. In der Regel in Tabellen gespeichert, ist es die einfachste Art von Daten, mit herkömmlichen Tools zu suchen, zu verwalten und zu analysieren. Beispiele: Finanztransaktionen, Point-of-Sale-Daten (POS), Patientenakten.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EUnstrukturiert:\u003C/b\u003E Besitzt kein vordefiniertes Format. Die häufigste Art von Big Data, stellt jedoch die größten Herausforderungen für die Analyse dar. Enthält Text, Bilder, Audio und Video. Beispiele: Social-Media-Daten, PDFs und E-Mails, Sensordaten von smarten Thermostaten oder Wearables.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003ESemistrukturiert\u003C/a\u003E:\u003C/b\u003E Eine Mischung aus den beiden anderen Arten. Sie besitzt keine starre Struktur wie strukturierte Daten, verfügt aber über einige organisatorische Eigenschaften, die die Kategorisierung und Analyse im Vergleich zu unstrukturierten Daten erleichtern. Beispiele XML- und JSON-Dateien, Protokolldateien, Webseiten.\u003C/p\u003E\n"},{"title":"Welche Beispiele für Datenverarbeitung gibt es?","richText":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ELohnabwicklung: \u003C/b\u003EAnhand von Mitarbeitendendaten, darunter Arbeitsstunden, Abzüge, Gehalts- und Steuerinformationen, werden Gehaltsschecks termingerecht berechnet und ausgestellt.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EE-Commerce-Empfehlungen:\u003C/b\u003E Wenn Sie in einem Online-Shop surfen, verarbeiten die Systeme dieses Unternehmens Ihren Suchverlauf, frühere Käufe usw., um Ihnen Produkte zu empfehlen, die Ihnen gefallen könnten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EWettervorhersage:\u003C/b\u003E Meteorologen verarbeiten Unmengen von Daten von Satelliten, Bodensensoren und Wetterstationen, um komplexe Modelle zu erstellen, die Wettermuster vorhersagen und Prognosen erstellen (obwohl die Genauigkeit Ihrer lokalen Vorhersage auf einem anderen Blatt 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