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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",":items":{"title_v2":{"id":"title-v2-826ba165fb","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-f19e6e5462","text":"\u003Cp\u003EIn einer Welt, in der sich Organisationen in zunehmendem Maße auf Daten stützen, ist der Bedarf an flexiblen, skalierbaren und kostengünstigen Lösungen für die Datenspeicherung und -Analytik so groß wie nie zuvor. Data Lakes haben sich zu einem Eckpfeiler moderner Dateninfrastrukturen etabliert, die das Volumen, die Vielfalt und die Geschwindigkeit der heute verfügbaren Daten bewältigen können.\u003C/p\u003E\n\u003Cp\u003EDieser Artikel untersucht nicht nur, was ein Data Lake ist, sondern geht auch auf die zugrunde liegende Architektur ein, die einen effektiven Betrieb mit großen Datenmengen sicherstellt.\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",":type":"snowflake-site/components/container"},"container_copy":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"data",":items":{"title_v2":{"id":"title-v2-d9ae423382","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist ein Data Lake?"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-65a752c7c0","text":"\u003Cp\u003EEin Data Lake ist ein zentrales Repository, in dem Organisationen alle Arten von Daten – strukturierte, semistrukturierte und unstrukturierte – in ihrem Rohformat speichern können. Im Gegensatz zu herkömmlichen Datenbanken oder Data Warehouses benötigen Data Lakes keine vordefinierten Schemata oder Formatierungen vor der Speicherung. Das macht sie äußerst flexibel und agil.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-db38ec24e7","type":"heading4","lines":["Wichtige Merkmale:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-ec9cf5d35e","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESchema-on-Read: \u003C/b\u003EDaten werden bei ihrem Zugriff ausgewertet, nicht wenn sie erfasst werden\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUnterstützung verschiedener Datentypen:\u003C/b\u003E von CSV-Dateien und JSON-Protokollen bis hin Bildern und Videos\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUnterstützung mehrerer Sprachen: \u003C/b\u003Ewie SQL, Spark und weitere\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESkalierbar und kosteneffizient: \u003C/b\u003Ebasieren auf preiswertem Objektspeicher, oft in der Cloud\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFür ein breites Anwenderspektrum:\u003C/b\u003E von Datenanalyst:innen zu Data Scientists und Data Engineers\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFür den gesamten Datenlebenszyklus: \u003C/b\u003Evon der Datenerfassung und -speicherung über die Verarbeitung und Transformation bis hin zu fortschrittlichen Analytics und KI.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"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",":items":{"title_v2":{"id":"title-v2-1db6bfa0bc","additionalClasses":"headline-decoration","type":"heading2","lines":["Data Lake im Vergleich zum Data Warehouse"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-1a034a9787","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEin \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003EData Lake und ein Data Warehouse\u003C/a\u003E werden beide zum Speichern und Analysieren von Daten eingesetzt, aber sie unterscheiden sich in wichtigen Punkten.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDatenstruktur:\u003C/b\u003E In einem Data Lake werden unverarbeitete Rohdaten gespeichert, die keine vordefinierte Struktur haben. Das System verwendet einen „Schema-on-Read“-Ansatz, bei dem die Daten erst beim Zugriff für die Analyse organisiert werden. In einem \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003EData Warehouse\u003C/a\u003E müssen die Daten vor der Speicherung bereinigt und umgewandelt werden, damit sie in ein vordefiniertes Schema passen. Dieser Prozess wird als „Schema-on-Write“ bezeichnet.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDatentypen: \u003C/b\u003EEin Data Lake kann alle Datentypen aufnehmen, einschließlich unstrukturierter, semistrukturierter und strukturierter Daten. In herkömmlichen Data Warehouses werden hauptsächlich strukturierte Daten gespeichert, die für die Analyse vorbereitet sind.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAnwender:innen und Verwendungszweck:\u003C/b\u003E Data Warehouses werden häufig für Business Intelligence (BI) und Reporting optimiert und von Business-Analyst:innen genutzt. Data Lakes werden häufig von Data Scientists und Data Engineers für fortschrittliche Analytics und maschinelles Lernen auf einer größeren Vielfalt an Datentypen verwendet.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EHybrider Ansatz:\u003C/b\u003E Viele Organisationen kombinieren beide Systeme in einer „Lakehouse“-Architektur, um die Flexibilität eines Data Lake mit der Performance eines Data Warehouse zu verbinden.\u003C/li\u003E\r\n\u003C/ul\u003E\r\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",":type":"snowflake-site/components/container"},"container_copy_copy__1697459514":{"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":"air",":items":{"title_v2":{"id":"title-v2-51cf9381ee","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile von Data Lakes"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-3005c41ba6","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEin Data Lake bietet viele Vorteile für Organisationen, die ihre Daten in großem Umfang auf moderne Weise verwalten und analysieren möchten.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EKosteneffiziente Speicherung: \u003C/b\u003EData Lakes basieren auf kostengünstigem Objektspeicher, oft in der Cloud, was im Vergleich zu herkömmlichen Systemen zu geringeren Speicherkosten führt. So können große Datenmengen ohne vordefinierte Struktur kostengünstig gespeichert werden.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESkalierbarkeit für \u003Ca href=\"https://www.snowflake.com/de/fundamentals/big-data/\" target=\"_blank\"\u003EBig Data\u003C/a\u003E: \u003C/b\u003EData Lakes sind darauf ausgelegt, das Volumen und die Vielfalt der heutigen Daten zu bewältigen, und können mühelos Petabytes an Daten unterstützen. Sie sind hochgradig skalierbar und können mit den Anforderungen Ihres Unternehmens mitwachsen.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFlexibilität für verschiedene Datentypen: \u003C/b\u003EEin Data Lake kann jede Art von Daten aus jeder Quelle und mit jeder Geschwindigkeit erfassen, unabhängig davon, ob es sich um \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Estrukturierte, semistrukturierte oder unstrukturierte\u003C/a\u003E Daten handelt. Dieser „Schema-on-Read“-Ansatz sorgt für maximale Flexibilität bei der Datenexploration.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUnterstützung für fortschrittliche Analytics und KI:\u003C/b\u003E Ein Data Lake dient als flexible Datengrundlage für eine breite Palette von analytischen Workloads. Es unterstützt fortschrittliche Analytik, maschinelles Lernen und Echtzeitanalytik. Die Fähigkeit, Rohdaten in großen Mengen bereitzustellen, ist für das Training von KI- und ML-Modellen unerlässlich.\u003C/li\u003E\r\n\u003C/ul\u003E\r\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",":type":"snowflake-site/components/container"},"container_copy_copy_1767623498":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":items":{"title_v2":{"id":"title-v2-485d4bae55","additionalClasses":"headline-decoration","type":"heading2","lines":["Was versteht man unter der Architektur von Data Lakes?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-a363120f43","text":"\u003Cp\u003EWährend ein Data Lake das Konzept ist, bezieht sich die Data-Lake-Architektur auf die zugrunde liegende Struktur und die Komponenten, die ein effizientes Funktionieren des Data Lake ermöglichen. Es handelt sich um ein mehrschichtiges System zur Verwaltung der Erfassung, Speicherung, Verarbeitung, Erkennung, Governance und Nutzung von großen Datasets.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-d0181baf97","type":"heading4","lines":["Kernkomponenten der Data-Lake-Architektur:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-425f59fd8c","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ESchicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EFunktionalität\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EErfassungsschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EBezieht Daten aus verschiedenen Quellen (Streaming, Batch, IoT, APIs)\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ESpeicherschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ESpeichert Rohdaten im skalierbaren Objektspeicher\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EMetadaten- und Katalogisierungsschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EIndiziert, kennzeichnet und organisiert Daten für die Auffindbarkeit\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EVerarbeitungsschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EÜbernimmt die \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003EDatenumwandlung\u003C/a\u003E mithilfe von Tools für die Batch- oder Echtzeit-Verarbeitung.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EZugriffsschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EErmöglicht Datenabfragen, Datenexploration und Analytik\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EGovernance- und Sicherheitsschicht\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EVerwaltet Datenschutz, Zugriffskontrollen, Audits und Compliance\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/text"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1520074195":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1202161416":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1835740287":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__746839706":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1923870193":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1477902138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"type",":items":{"title_v2":{"id":"title-v2-06fd10eda3","type":"heading2","lines":["Unterstützte Datentypen"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-2419466acc","text":"\u003Cp\u003EEin Data Lake ist für die Speicherung großer Datenmengen in ihrem ursprünglichen Rohformat ausgelegt und unterstützt daher eine große Vielfalt an Datentypen. Diese lassen sich grob in drei Haupttypen einteilen:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-d90943d87b","type":"heading4","lines":["Strukturierte Daten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1835740287":{"id":"text-f20911d081","text":"\u003Cp\u003EDiese Kategorie von Daten ist hochgradig organisiert und lässt sich übersichtlich in Zeilen und Spalten einordnen, ähnlich wie eine relationale Datenbank oder eine Tabellenkalkulation. Es gibt ein vordefiniertes Schema, das die Suche, Analyse und Verwaltung erleichtert. Beispiele für strukturierte Datentypen sind:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-877ec0216f","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ERelationale Datenbanktabellen: \u003C/b\u003EDaten, die in festen Spalten und Zeilen angeordnet sind\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EKalkulationstabellen (z. B. CSV-Dateien):\u003C/b\u003E Tabellarische Daten mit definierten Spalten\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ENumerische Daten:\u003C/b\u003E Ganze Zahlen, Gleitkommazahlen, Dezimalstellen\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EKategorisierte Daten: Labels oder Kategorien mit einer begrenzten Anzahl von Werten\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDatums- und Uhrzeitdaten:\u003C/b\u003E Zeitstempel, Datumsangaben und Zeitwerte\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1923870193":{"id":"title-v2-77e1d5b0e3","type":"heading4","lines":["Semistrukturierte Daten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1477902138":{"id":"text-36cafef3c4","text":"\u003Cp\u003EDiese Daten folgen keiner starren tabellarischen Struktur, haben aber einige organisatorische Eigenschaften, wodurch sie leichter zu analysieren sind als unstrukturierte Daten. Sie enthalten oft Tags oder Markierungen, die semantische Elemente trennen und Hierarchien durchsetzen. Beispiele sind unter anderem:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy":{"id":"text-83a0ac2d32","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-json/\"\u003EJSON\u003C/a\u003E (JavaScript Object Notation): \u003C/b\u003Eein kompaktes Format mit Schlüssel-Wert-Paaren und verschachtelten Objekten\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EXML (eXtensible Markup Language): \u003C/b\u003Eeine Markup-Sprache, die eine Reihe von Regeln für die Kodierung von Dokumenten in einem für Menschen und Maschinen lesbaren Format definiert\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECSV („comma separated values“) mit komplexen Strukturen: \u003C/b\u003Eist zwar grundsätzlich strukturiert, kann aber auch semistrukturiert sein, mit einer unterschiedlichen Anzahl von Spalten oder verschachtelten Daten innerhalb von Feldern\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EProtokolldateien: \u003C/b\u003Eenthalten oft Zeitstempel, Ereignistypen und Meldungen, die geparst werden können\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENoSQL-Datenbanken:\u003C/b\u003E Dokumente oder Schlüsselwertspeicher, bei denen das Schema zwischen den Einträgen variieren kann\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EHTML (HyperText Markup Language):\u003C/b\u003E obwohl es hauptsächlich für Webseiten gedacht ist, enthält es strukturierte Elemente und Daten\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EYAML (YAML Ain't Markup Language™):\u003C/b\u003E ein für Menschen verständlicher Daten-Serialisierungsstandard für alle Programmiersprachen\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1520074195":{"id":"title-v2-69a7161335","type":"heading4","lines":["Unstrukturierte Daten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__746839706":{"id":"text-19f53a7bb6","text":"\u003Cp\u003EDiese Daten haben kein vordefiniertes Format und keine vordefinierte Organisation, was ihre Analyse mit herkömmlichen Methoden erschwert. Um Erkenntnisse zu gewinnen, sind oft spezielle Tools und Techniken wie die Verarbeitung natürlicher Sprache oder maschinelles Lernen erforderlich. Beispiele für unstrukturierte Datentypen sind:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy_":{"id":"text-f415e943e9","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ETextdateien:\u003C/b\u003E Dokumente (.txt, .doc, .pdf), E-Mails, Social-Media-Beiträge\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBilddateien:\u003C/b\u003E JPEG, PNG, GIF\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAudiodateien: \u003C/b\u003EMP3, WAV\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EVideodateien:\u003C/b\u003E MP4, AVI, MOV\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESensordaten:\u003C/b\u003E Datenströme von IoT-Geräten, die möglicherweise keine einheitliche Struktur haben\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBinärdateien:\u003C/b\u003E Ausführbare Dateien, proprietäre Datenformate\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1202161416":{"id":"text-b15105c146","text":"\u003Cp\u003EDie Fähigkeit, all diese verschiedenen Datentypen in ihrem nativen Format zu speichern und zu verarbeiten, ist ein wichtiges Merkmal und ein Vorteil eines Data Lake. Dieser „Schema-on-Read“-Ansatz sorgt für Flexibilität und versetzt Data Scientists und Analyst:innen in die Lage, Daten auf verschiedene Weise zu untersuchen und zu analysieren, ohne den Zwängen einer vordefinierten Struktur unterworfen zu sein.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","title_v2_copy_copy_c","text_copy_860073138__1835740287","text_copy_copy","title_v2_copy_copy_c_1923870193","text_copy_860073138__1477902138","text_copy_copy_copy","title_v2_copy_copy_c_1520074195","text_copy_860073138__746839706","text_copy_copy_copy_","text_copy_860073138__1202161416"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_981691511":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-d9353fccfa",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1340528379":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"life",":items":{"title_v2":{"id":"title-v2-f0f763ed22","type":"heading2","lines":["Datenlebenszyklus"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-a89f2492f6","text":"\u003Cp\u003EIn einem Data Lake beschreibt der Datenlebenszyklus die Phasen, welche die Daten von ihrer Erstellung oder Erfassung bis zu ihrer Archivierung oder Löschung durchlaufen. Es handelt sich um einen kontinuierlichen Prozess, der dazu beiträgt, dass die Daten während ihrer gesamten Existenz innerhalb des Data Lake effektiv verwaltet, genutzt und kontrolliert werden. Hier ein Überblick über den typischen Lebenszyklus von Daten in einem Data Lake – mit dem Hinweis, dass spezifische Implementierungen variieren können:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1340528379":{"id":"text-d1330ab2cb","text":"\u003Cp\u003E\u003Cb\u003E1. Datenerfassung: \u003C/b\u003EDies ist die erste Phase, in der Daten aus verschiedenen Quellsystemen in den Data Lake eingebracht werden. Diese Quellen können vielfältig sein, einschließlich strukturierter Datenbanken, semistrukturierter Protokolle, unstrukturierter Dokumente, Streaming-Daten von IoT-Geräten, Social Media Feeds und mehr. Das Hauptmerkmal der Datenerfassung in einem Data Lake ist häufig die „Erfassung im Ist-Zustand“, d. h. die Daten werden üblicherweise in ihrem nativen Rohformat ohne nennenswerte Umwandlung oder Schemadefinition im Vorfeld geladen. Auf diese Weise entsteht ein Maximum an Flexibilität für zukünftige Analysen. Zu den Tools und Prozessen, die in dieser Phase zum Einsatz kommen, gehören das Laden von Batches, das Erfassen von Echtzeit-Streams und Datenkonnektoren.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2. Datenspeicherung und Persistenz:\u003C/b\u003E Nach der Erfassung werden die Rohdaten im Data Lake gespeichert. Die Architektur nutzt häufig verteilte Speichersysteme wie HDFS (Hadoop Distributed File System) oder Cloud-basierte Objektspeicher (z. B. Amazon S3, Azure Data Lake Storage, Google Cloud Storage). Die Daten bleiben in ihrem ursprünglichen Format erhalten und bilden so die Grundlage für verschiedene Analyseansätze. Die Skalierbarkeit und Kosteneffizienz der Speicherschicht sind entscheidend für die Handhabung der potenziell riesigen Datenmengen in einem Data Lake. Je nach Häufigkeit des Datenzugriffs und den Aufbewahrungsrichtlinien können verschiedene Speicherschichten eingesetzt werden.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3. Verarbeitung und Umwandlung von Daten: \u003C/b\u003EIn dieser Phase werden die Rohdaten für die Analyse und die Nutzung vorbereitet. Je nach dem spezifischen analytischen Anwendungsfall können die Daten auf verschiedene Weise umgewandelt werden, z. B. durch Bereinigung, Filterung, Zusammenführung, Aggregation und Anreicherung. An diesem Punkt kommt „Schema-on-Read“ ins Spiel – das Schema kommt zur Anwendung, wenn die Daten für einen bestimmten Zweck verarbeitet werden, und nicht zum Zeitpunkt der Erfassung. In dieser Phase kommen verschiedene Verarbeitungs-Engines und Frameworks zum Einsatz, z. B. Spark, Hadoop MapReduce, Data-Warehouse-Tools, die mit dem Lake verbunden sind, und serverlose Compute-Services.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E4. Datenexploration und -analyse: \u003C/b\u003EHier untersuchen Data Scientists, Analyst:innen und geschäftliche Benutzer:innen die verarbeiteten Daten, um Muster zu entdecken, Erkenntnisse zu gewinnen und Geschäftsfragen zu beantworten. Sie können eine Vielzahl von Tools und Techniken einsetzen, darunter SQL-ähnliche Abfragen, Tools zur \u003Ca href=\"https://www.snowflake.com/de/fundamentals/data-visualization/\" target=\"_blank\"\u003EDatenvisualisierung\u003C/a\u003E, statistische Analysepakete und Algorithmen für maschinelles Lernen. Dank der Flexibilität des Data Lake können je nach den spezifischen Anforderungen verschiedene analytische Ansätze für dieselben Daten zur Anwendung kommen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E5. Datennutzung und Aktion: \u003C/b\u003EDie Erkenntnisse und verarbeiteten Daten werden dann von verschiedenen nachgelagerten Applikationen und Anwender:innen genutzt. Dazu gehören die Erstellung von Berichten und Dashboards, die Einspeisung von Daten in operative Systeme, die Bereitstellung von Echtzeitanwendungen oder die Unterstützung von Geschäftsentscheidungen. Die genutzten Daten können je nach der verarbeitenden Anwendung in verschiedenen Formaten vorliegen und über unterschiedliche Schnittstellen zugänglich sein.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E6. Data Governance und Sicherheit:\u003C/b\u003E Während des gesamten Lebenszyklus sind Data Governance und Sicherheit von entscheidender Bedeutung. Dazu gehören die Definition und Durchsetzung von Richtlinien in Bezug auf \u003Ca href=\"https://www.snowflake.com/de/fundamentals/a-practical-guide-to-data-quality/\"\u003EDatenqualität\u003C/a\u003E, Metadatenmanagement, Datenherkunft, Zugriffskontrolle, Datenmaskierung und Einhaltung von Vorschriften. Eine wirksame Governance trägt dazu bei, dass die Daten im Data Lake vertrauenswürdig und sicher sind und ordnungsgemäß verwaltet werden.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E7. Archivierung und Löschung von Daten: \u003C/b\u003EWenn Daten altern und ihr Geschäftswert abnimmt, müssen sie möglicherweise aus Gründen der Compliance oder zur Optimierung der Speicherkosten archiviert werden. Nicht mehr benötigte Daten können gemäß definierten Aufbewahrungsrichtlinien gelöscht werden. Diese letzte Phase sorgt dafür, dass der Data Lake effizient und langfristig regelkonform ist.\u003C/p\u003E\r\n\u003Cp\u003EIm Wesentlichen ist der Datenlebenszyklus in einem Data Lake auf Flexibilität und Anpassungsfähigkeit ausgelegt. So können Organisationen große Mengen unterschiedlicher Daten erfassen, sie nach Bedarf für bestimmte analytische Anwendungsfälle verarbeiten und wertvolle Erkenntnisse gewinnen, ohne dabei auf angemessene Governance und Sicherheit verzichten zu müssen. Das „Schema-on-Read“-Prinzip und die Trennung von Speicher und Rechenressourcen sind die Hauptmerkmale, durch die sich dieses System von herkömmlichen Data-Warehousing-Ansätzen unterscheidet.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","text_copy_860073138__1340528379"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"flexible_column_cont":{"id":"flexible-column-container-086e2f728f","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-5ef73014b4",":items":{"card_v2_copy":{"id":"card-v2-5d494c492f","modalId":"video","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["How Elysium Analytics Enhances Its Semantic Security Data Lake with Snowflake Native Apps"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=rznH2KyV6MY"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Video ansehen"},"image":{"id":"image","lazyEnabled":true,"alt":"elysium logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a62365e1-1fbd-4c9e-9dd1-7e7f8d1b749b/elysium%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-58ab36e457",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["container_copy_copy_","flexible_column_cont"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_copy_copy__1663285187":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1885071390":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":items":{"title_v2":{"id":"title-v2-07269d6201","additionalClasses":"headline-decoration","type":"heading2","lines":["Die Bedeutung von Data Lakes"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-d6631147b3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EOb konzeptionell oder aus dem Blickwinkel der Architektur betrachtet, der Wert von Data Lakes liegt in ihrer Fähigkeit, moderne Datenanforderungen zu erfüllen. Die wichtigsten Vorteile von Data Lakes sind: \u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESkalierbarkeit:\u003C/b\u003E mühelose Unterstützung von Petabytes an Daten\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFlexibilität: \u003C/b\u003EErfassung beliebiger Daten, aus beliebigen Quellen und mit beliebiger Geschwindigkeit\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EKosteneinsparungen:\u003C/b\u003E geringere Speicherkosten im Vergleich zu herkömmlichen Systemen\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFortschrittliche Analytics: \u003C/b\u003EDatengrundlage für KI, maschinelles Lernen und Echtzeit-Analytik\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDatendemokratisierung:\u003C/b\u003E umfassender Zugang zu Daten für technische und nicht-technische Teams\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-4b71e38370","type":"heading4","lines":["Die Entwicklung zum modernen Data Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613_1885071390":{"id":"text-e7c3a98121","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EHerkömmliche Data Lakes entwickeln sich zu modernen Data-Lake-Architekturen, die oft als „Lakehouses“ bezeichnet werden – hybride Systeme, die die flexible Speicherung von Data Lakes mit der strukturierten Abfrage und der Performance von Data Warehouses kombinieren.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETrends in der modernen Data-Lake-Architektur:\u003C/b\u003E\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ECloud-native Bereitstellung\u003C/li\u003E\n\u003Cli\u003EIntegrierte Verarbeitungs-Engines\u003C/li\u003E\n\u003Cli\u003EIntegrierte Governance-Frameworks\u003C/li\u003E\n\u003Cli\u003EEinheitliche Plattformen, die die Kluft zwischen Data Lake und Data Warehouse überbrücken\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text_copy_1241045613_1885071390"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_copy__967194517":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1885071390":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_422171653":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":items":{"title_v2":{"id":"title-v2-2e02599deb","additionalClasses":"headline-decoration","type":"heading2","lines":["Herausforderungen und Anwendungsfälle für Data Lakes"],":type":"snowflake-site/components/title-v2"},"title_v2_copy_copy_c":{"id":"title-v2-eecc40e00d","type":"heading4","lines":["Herausforderungen im Zusammenhang mit Data Lakes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_1241045613_1885071390":{"id":"text-4d1bd99930","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EDatenauffindbarkeit: \u003C/b\u003EDas Fehlen einer angemessenen \u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-a-data-catalog/\" target=\"_blank\" rel=\"noopener 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