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Hong"}}],"image":{"id":"image-5818dabd6d","height":"572","src":"/adobe/dynamicmedia/deliver/dm-aid--c87aeaed-47cd-4c7e-8e27-12eda74d922d/excelsheetshead.png?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"1024",":type":"snowflake-site/components/image"},"timeToRead":"9","publicationDate":"NOV 05, 2026","tag":{"tagText":"KI und ML","tagColor":"#C54985"},"title":{"lines":["Kontrollierte KI für Tabellenkalkulationen: Ankündigung von Snowflake CoCo für Microsoft Excel und Google Sheets"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 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aem-GridColumn--default--12","blog_component_16":"aem-GridColumn aem-GridColumn--default--12","blog_component_15":"aem-GridColumn aem-GridColumn--default--12","blog_component_14":"aem-GridColumn aem-GridColumn--default--12","blog_component_13":"aem-GridColumn aem-GridColumn--default--12","blog_component_12":"aem-GridColumn aem-GridColumn--default--12","blog_component_22":"aem-GridColumn aem-GridColumn--default--12","blog_component_21":"aem-GridColumn aem-GridColumn--default--12","blog_component_20":"aem-GridColumn aem-GridColumn--default--12"},"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_component_0":{"id":"blog-text-957faba52b","text":"\u003Cp\u003E​​\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_2":{"id":"blog-text-a4435a63f5","text":"\u003Cp\u003EWir freuen uns, Snowflake CoCo, den Coding-Agenten für Datenteams, auf zwei der weltweit am häufigsten genutzten Analyseoberflächen bereitzustellen: Microsoft Excel und Google Sheets. Diese Angebote integrieren Governed Snowflake-Daten, Semantik und KI nativ in die Tabellenkalkulationsoberfläche, die Analyst:innen bereits nutzen. Fachanwender:innen interagieren mit CoCo in natürlicher Sprache, um Workbooks mit nativen Formeln, Diagrammen und Pivot-Tabellen zu erstellen – ohne sich um alltägliche Zellenformatierung und Datenintegration kümmern zu müssen. Da Agenten zur Schnittstelle für die Erstellung von Tabellenkalkulationen werden, können Nutzer:innen mit den Tools, die sie bereits verwenden, effizienter mehr erreichen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"blog-title-8ee641cf46","propertiesId":"manuelle-exporte-und-konnektoren-sind-muhsam","type":"heading2","lines":["Manuelle Exporte und Konnektoren sind mühsam"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_4":{"id":"blog-text-9ac2a76991","text":"\u003Cp\u003ETabellenkalkulationen sind seit über 40 Jahren die dominierende Schnittstelle für die Interaktion mit Daten. Trotz dieser Allgegenwart finden es Teams schwierig, konsistente Daten in ihren Tabellenkalkulationen zu haben – wobei Dateinamen, Kommentare und E-Mails verschiedene Versionen und Quellen kennzeichnen. Typischerweise haben Nutzer:innen drei Optionen, um Daten zu verbinden:\u003C/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECSV-Exporte\u003C/strong\u003E: Nutzer:innen von Tabellenkalkulationen laden Abfrageergebnisse, Tabellen oder Notebook-Ausgaben lokal auf ihr Gerät herunter.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EODBC-Konnektoren\u003C/strong\u003E: Nutzer:innen von Tabellenkalkulationen bitten ihr Datenteam, ODBC-Treiber zu konfigurieren, um über SQL eine Verbindung zu ihren Daten herzustellen.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDrittanbieter-Konnektoren\u003C/strong\u003E: Nutzer:innen von Tabellenkalkulationen bitten ihr IT-Team, ein separates System für die Datenverbindung bereitzustellen und zu warten.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EManuelle Exporte führen zu unzusammenhängenden Datenkopien und schaffen Datensilos. Für administrative Teams schafft dies Governance-Herausforderungen im Zusammenhang mit der Datenausbreitung. Für Fachanwender:innen führt dies zu inkonsistenten Analysen und isolierter Arbeit.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"blog-title-d2a0724905","propertiesId":"ki-ist-eine-erwartung-fur-produktive-arbeit","type":"heading3","lines":["KI ist eine Erwartung für produktive Arbeit"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_6":{"id":"blog-text-c204b82264","text":"\u003Cp\u003EEntscheidend ist, dass die Einschränkungen manueller Datenkonnektoren die Integration von KI in Tabellenkalkulationen erschweren. Wir gehen davon aus, dass Agenten zum Standardmodus für die Arbeit mit Tabellenkalkulationen werden, ähnlich wie bei anderen Schnittstellen wie der integrierten Entwicklungsumgebung (IDE) für die Programmierung. KI kann die Routineaufgaben bei der Erstellung eines Workbook übernehmen, wie das Abrufen und Bereinigen von Daten, das Schreiben komplexer Formeln und das Erstellen von Modellen – so kann sich der:die Analyst:in auf die Analyse konzentrieren.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_7":{"id":"image-dfe65a4d64","height":"1028","src":"/adobe/dynamicmedia/deliver/dm-aid--90a069e6-6eba-4b9a-a0a1-8fe877fd93e2/three-methods-to-export-data-from-snowflake-to-spreadsheets.png?preferwebp=true&quality=85","alt":"Diagram showing three methods to export data from Snowflake to spreadsheets with governance trade-offs","lazyEnabled":true,"isLcpImage":true,"width":"1999","title":"Figure 1: Traditional export and data connectors are challenging to use and are difficult to integrate with AI.",":type":"snowflake-site/components/image"},"blog_component_8":{"id":"blog-title-aa1c243ff8","propertiesId":"erleben-sie-snowflake-mit-ki-jetzt-in-ihrer-tabellenkalkulation","type":"heading2","lines":["Erleben Sie Snowflake mit KI, jetzt in Ihrer Tabellenkalkulation"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_ins":{"id":"wistia-video-6fbe4638ba","title":{"id":"title","type":"heading1","lines":["Kontrollierte KI für Tabellenkalkulationen: Ankündigung von Snowflake CoCo für Microsoft Excel und Google Sheets"],":type":"snowflake-site/components/title"},"wistiaVideoId":"9u5hsiejs8","showVideoLength":false,"showRewatchButton":false,":type":"snowflake-site/components/wistia-video"},"blog_component_9":{"id":"blog-text-68e76400a7","text":"\u003Cp\u003EAbbildung 2: CoCo erledigt agentenbasiert Finanzarbeiten in Excel (in 4-facher Geschwindigkeit) und erstellt ein Leveraged-Buyout-Modell mit Diagrammen, Pivot-Tabellen und mehr unter Verwendung von Snowflake-Daten.\u003C/p\u003E\n\u003Cp\u003ESnowflake CoCo für Microsoft Excel und Google Sheets bringt Snowflake in die Tabellenkalkulation, in der Analyst:innen arbeiten, ohne dass sie ein völlig neues und anderes Tool erlernen müssen. CoCo erstellt und führt Abfragen für Snowflake-Daten mit den Berechtigungen der Nutzer:innen aus, wodurch herkömmliche Konnektoren überflüssig werden. CoCo erstellt dann Workbooks und verwendet Zellen als Kontext, um native Tabellenkalkulationsobjekte zu erstellen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"blog-title-a1cc7995d2","propertiesId":"live-governed-snowflake-daten-mit-intelligenter-aktualisierung","type":"heading2","lines":["Live-Governed-Snowflake-Daten mit intelligenter Aktualisierung"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_11":{"id":"blog-text-0f88d51c03","text":"\u003Cp\u003ECoCo für Excel und Sheets liefert Live-Snowflake-Daten direkt in Ihre Tabellenkalkulation und \u003Ca href=\"https://www.snowflake.com/de/blog/governed-ai-enterprise-controls-snowflake-coco/\"\u003Ebehält dabei die Enterprise-Governance\u003C/a\u003E und den Kontext bei. Nachdem sich Nutzer:innen angemeldet haben, verwendet CoCo ihre ausgewählte oder standardmäßige Snowflake-Rolle, um ihren Zugriff einzuschränken. \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/rbac-simplified-inherited-grants-access-requests/\"\u003ESnowflake-Zugriffskontrollen\u003C/a\u003E, Maskierung und Row Access Policies gelten für die Ergebnisse, die in Ihre Tabellenkalkulation geschrieben werden. Diese Daten- und KI-Abfragen profitieren von Ihrem gesamten Snowflake-Kontext, einschließlich Semantic Views, Business Ontology und Snowflake Cortex Sense, die darauf ausgelegt sind, Analyst:innen konsistente, genaue Ergebnisse zu liefern – entscheidend bei der Verwendung von KI für vertrauenswürdige Analysen.\u003C/p\u003E\u003Cp\u003ENachdem Daten in eine Tabellenkalkulation abgerufen wurden, macht es die intelligente Aktualisierung einfach, die Daten auf dem neuesten Stand zu halten. Wenn CoCo Abfragen zum Abrufen von Daten ausführt, speichert es den Abfragetext in den Einstellungen der Workbooks. Wenn CoCo Daten aktualisiert, kann es das Workbook intelligent aktualisieren, um sich ändernde Ergebnisgrößen zu berücksichtigen. Nutzer:innen können die Aktualisierung auf zwei Arten auslösen:\u003C/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EManuell\u003C/strong\u003E: Dies geschieht über eine Schaltfläche oder den Skill \u003Ccode\u003E/refresh\u003C/code\u003E. Dies ist für Analyst:innen gedacht, um ihre Modelle mit den neuesten Daten zu aktualisieren.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAutomatisch\u003C/strong\u003E: Dies geschieht, wenn das Workbook geöffnet wird. Dies ist für Führungskräfte gedacht, die ein Workbook öffnen und immer über die neuesten Daten verfügen möchten, ohne etwas anklicken oder konfigurieren zu müssen. Dies wird nur für CoCo für Excel unterstützt.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EWenn sich die Abfrage ändert, z. B. bei einer anderen Ergebnisgröße oder einer anderen Gruppe von Spalten, die von einer SELECT *-Anweisung zurückgegeben werden, aktualisiert CoCo das Workbook, um vorhandene Daten, Formeln und Assets intakt zu halten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_12":{"id":"image-c4eeb36d44","height":"800","src":"/adobe/dynamicmedia/deliver/dm-aid--70f10c7a-fd69-4914-9927-31a17b59b8b2/before-and-after-states-of-a-data-refresh-workflow-with.png?preferwebp=true&quality=85","alt":"Diagram showing before and after states of a data refresh workflow with regional heatmaps and three steps: first pull, workbook saved query, and rerun query","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 3: CoCo refreshes the workbook using saved queries to pull the latest data.",":type":"snowflake-site/components/image"},"blog_component_13":{"id":"blog-text-beeeb92d21","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDa alle Abfragen in dem Workbook gespeichert werden, haben Nutzer:innen einen klaren Überblick darüber, welche Daten-Assets für die Analyse verwendet wurden. Dies macht es einfach, verschiedene Antworten über Workbooks hinweg abzugleichen und endlich die Frage zu beantworten, welcher Zahl wir vertrauen können.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_14":{"id":"blog-title-cb849531ad","propertiesId":"coco-im-workbook-ermoglicht-natural-language-analytics-agentic-manipulation-und-ki-funktionen","type":"heading2","lines":["CoCo im Workbook ermöglicht Natural Language Analytics, Agentic Manipulation und KI-Funktionen"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-dfc68f091c","text":"\u003Cp\u003ECoCo bringt Agentic Workflows mit, um Tabellen direkt zu manipulieren und geht damit über Chatbot-Integrationen und Datenkonnektoren hinaus. Anstatt Fragen in einem Seitenbereich zu beantworten, generiert CoCo die Ausgabe nativ in der Tabellenkalkulation mit Formeln und Objekten. In Excel können Nutzer:innen unterstützte Snowflake Cortex AI-Funktionen direkt in Tabellenzellen aufrufen, wie z. B. \u003Ccode\u003ECORTEX.CLASSIFY\u003C/code\u003E und \u003Ccode\u003ECORTEX.SUMMARIZE\u003C/code\u003E.\u003C/p\u003E\u003Cp\u003ESie können CoCo beispielsweise bitten, den vierteljährlichen Umsatz nach Region und Wachstumsprognosen für Ihre Verkaufsdaten anzuzeigen. In Sekundenschnelle übersetzt CoCo dies in einen Plan zur Erstellung einer Analyse, komplett mit Formeln und Pivot-Tabellen:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"image-e8b58c474d","height":"1231","src":"/adobe/dynamicmedia/deliver/dm-aid--b2f9f20e-240c-4066-9731-0ad15dec8287/excel-spreadsheet-showing-arr-bridge-analysis-with-formulas.png?preferwebp=true&quality=85","alt":"Excel spreadsheet showing ARR Bridge analysis with formulas, clustered column chart, pivot table, and AI assistant panel","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 4: CoCo builds charts, pivot tables and formulas to analyze sample sales data from a semantic view.",":type":"snowflake-site/components/image"},"blog_component_17":{"id":"blog-text-c5376d9ded","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDiese Agentic-Funktionen erstrecken sich auf die Oberfläche, in der Analyst:innen mit den zentralen Analyseobjekten arbeiten, die Nutzer:innen von Tabellenkalkulationen erstellen. Da CoCo Zugriff auf Ihre Skills, Ihr Workbook und Ihre Daten als Kontext hat, kann es so aufbauen, wie es ein:e menschliche:r Analyst:in tun würde:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EVisualisierungen\u003C/b\u003E: CoCo analysiert die Form der Daten, um den effektivsten Diagrammtyp auszuwählen. Diagramme bleiben vollständig live, interaktiv und mit der Tabellenkalkulation verbunden, um eine einfache Bearbeitung zu ermöglichen.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPivot-Tabellen:\u003C/b\u003E CoCo nutzt die Rechenressource von Snowflake für die anspruchsvolle Datenmodellierung und Aggregationen zum Erstellen von Pivots, selbst bei Quelldatengrößen, die das Limit von 1 Million Zeilen in Excel und 20 Millionen Zellen in Sheets überschreiten.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFormatierung und Validierung:\u003C/b\u003E CoCo wendet automatisch bedingte Formatierung, präzise Zahlenformate und Datenvalidierungsregeln an. Diese können mit Skills angepasst werden, die spezifisch für Ihren Workflow und Ihre Praktiken sind.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDas Chefs Warehouse stellte fest, dass die Agentic-Funktionen von CoCo für Excel die Verwaltung ihrer komplexen Tabellendaten einfach und intuitiv machten:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_ins_b":{"id":"quote-item-931ce55777","alignment":"left","layout":"card","quoteSourceName":"Matt Wyman","quoteSourceTitle":"Vice President of Information Technology, The Chefs’ Warehouse","showQuoteIcon":true,"quote":"CoCo for Excel’s performance and capabilities are impressive. Our data includes many complex tables, and CoCo understands the relationships and structure to execute the right joins and build workbooks. Best of all, it’s intuitive, so our analysts can use it without training.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_18":{"id":"blog-title-a5752290d6","propertiesId":"nativ-in-die-snowflake-datenplattform-integriert-einschlielich-kontext","type":"heading2","lines":["Nativ in die Snowflake-Datenplattform integriert, einschließlich Kontext"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_19":{"id":"blog-text-844b2d2078","text":"\u003Cp\u003ECoCo lässt sich eng in Ihren Kontext in Snowflake integrieren, einschließlich Semantic Views, Business Ontology und Cortex Sense. Unter Verwendung des Snowflake-Kontexts stützt CoCo Analysen auf die Business Ontology Ihres Unternehmens, um Entitätsbeziehungen und -definitionen zu verstehen, wobei Semantic Views Konzepte mit Daten verbinden. Darüber hinaus lässt sich CoCo in Cortex Sense integrieren, um automatisch den Long Tail von Daten-Assets abzudecken, die besonders für Ad-hoc-Analysen nützlich sind. Viele Teams kuratieren bereits Kontext für ihre KI- und BI-Anwendungen, was sich nun direkt auf Excel und Sheets erstreckt.\u003C/p\u003E\u003Cp\u003EDarüber hinaus kann CoCo intensive, komplexe Berechnungen auf die skalierbaren virtuellen Warehouses von Snowflake auslagern. Traditionell baten Nutzer:innen bei großen Datenquellen ihr Datenteam, Pipelines zu erstellen, um benutzerdefinierte Datenprodukte zu kuratieren. Mit CoCo für Excel und Sheets werden komplexe Abfragen in einem Snowflake-Warehouse ausgeführt, anstatt direkt in der Tabellenkalkulationsoberfläche im Client. Dies bewältigt Arbeiten, die das überschreiten, was die Tabellenkalkulationsoberfläche aufnehmen oder berechnen kann.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_20":{"id":"blog-title-1ab1a78098","propertiesId":"coco-wurde-fur-die-arbeit-mit-tabellenkalkulationen-mit-snowflake-daten-entwickelt","type":"heading2","lines":["CoCo wurde für die Arbeit mit Tabellenkalkulationen mit Snowflake-Daten entwickelt"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_21":{"id":"blog-text-3a5a2abe06","text":"\u003Cp\u003ECoCo ist \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/data-eng-bench-data-engineering-agent-benchmark/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Espeziell für effiziente Datenarbeit entwickelt\u003C/a\u003E. Wir haben diese Grundlage auf Excel und Sheets erweitert, mit Tools zum Überprüfen von Workbooks, Schreiben von Formeln, Formatieren von Zellen und Erstellen von Diagrammen und Pivot-Tabellen.\u003C/p\u003E\u003Cp\u003EModellierungsaktionen sind jedoch nur ein Teil des Workflows. Die Beantwortung einer Geschäftsfrage erfordert auch den Zugriff auf und das Verständnis der zugrunde liegenden Daten. CoCo kombiniert die Snowflake-Objekterkennung und den Zugriff auf semantische Informationen mit Workbook-spezifischen Tools und hilft Nutzer:innen, von einer Frage zu relevanten Daten und Analysen zu gelangen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_ins_d":{"id":"quote-item-3a97025599","alignment":"left","layout":"card","quoteSourceName":"Kate Savina","quoteSourceTitle":"Head of Data Technology, GTCR LLC","showQuoteIcon":true,"quote":"Many AI tools can work in a spreadsheet, but CoCo for Excel is built for our work and starts with our data directly. There’s no exporting or switching of tools; it gets to the right answer faster, and our teams spend less time rebuilding reports.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_ins_c":{"id":"blog-text-36dc985d3e","text":"\u003Cp\u003EUm den End-to-End-Tabellenkalkulations-Workflow zu bewerten, haben wir intern gängige Tabellenkalkulationsaufgaben in Excel getestet, die logisches Denken, Modellierung und das Abrufen von Snowflake-Daten umfassten.\u003Csup id=\"footnote-ref-1\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003E Wir haben die Performance in drei Dimensionen (Genauigkeit, Latenz und Ausgabe-Token pro Aufgabe) sowohl für CoCo für Excel als auch für ein KI-Modell-Harness für Excel unter Verwendung des Snowflake MCP in drei Durchläufen gemessen. Die Add-ins wiesen bei diesen Aufgaben eine ähnliche Genauigkeit auf, aber CoCo für Excel hatte eine bessere Latenz und verwendete weniger Ausgabe-Token:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_22":{"id":"image-fcfece7994","height":"1000","src":"/adobe/dynamicmedia/deliver/dm-aid--38b51d3b-3097-4303-996e-17fad00418c1/scatter-plot-comparing-coco-for-excel-vs-ai-model-harness.png?preferwebp=true&quality=85","alt":"Scatter plot comparing CoCo for Excel vs AI model harness performance across three models, showing speed and token efficiency","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 5: In our testing, CoCo for Excel was faster and used fewer tokens at similar accuracy compared to an AI model harness for Excel using the Snowflake MCP across the same models.",":type":"snowflake-site/components/image"},"blog_component_23":{"id":"blog-text-c8a2412ac5","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ECoCo für Excel ist für die Arbeit mit Daten optimiert, einschließlich Objekterkennung, Verständnis und Skalierung. CoCo orchestriert Direct-to-Sheet-Datenübertragungen, die Abfrageergebnisse direkt in das Workbook streamen, ohne das Kontextfenster des Modells zu verwenden. Diese Architektur ist für die Manipulation und Analyse von Daten in großem Maßstab konzipiert und nutzt die Rechenressource und Asset-Integrationen von Snowflake:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_24":{"id":"image-1f74ad947f","height":"1000","src":"/adobe/dynamicmedia/deliver/dm-aid--edf0308a-0970-4520-804a-46d4ecff794f/line-chart-comparing-task-completion-time-versus-rows.png?preferwebp=true&quality=85","alt":"Line chart comparing task completion time versus rows written for CoCo Excel and AI model harness across three models","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 6: In our testing, CoCo for Excel has near-constant performance as data size scales, unlike an AI model harness for Excel using the Snowflake MCP.",":type":"snowflake-site/components/image"},"blog_component_25":{"id":"blog-text-815afeb5e0","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDiese internen Ergebnisse spiegeln das Designziel von CoCo als Coding-Agent für Datenteams wider – die Beibehaltung der Genauigkeit bei verbesserter Latenz und weniger Ausgabe-Token pro Aufgabe. Der Datenkontext untermauert die Analyse, während die optimierte Datenübertragung die für die Bereitstellung erforderliche Modellarbeit reduziert.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_26":{"id":"blog-title-54a0a4f632","propertiesId":"nutzen-sie-snowflake-coco-fur-excel-und-sheets-noch-heute","type":"heading2","lines":["Nutzen Sie Snowflake CoCo für Excel und Sheets noch heute"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_27":{"id":"blog-text-b9f7d0a64f","text":"\u003Cp\u003ENutzer:innen von Tabellenkalkulationen können jetzt mit Snowflake CoCo für Microsoft Excel und Google Sheets tiefere Einblicke gewinnen und tiefere Analysen durchführen – eine einfache, vertrauenswürdige und effiziente KI für Tabellenkalkulationen. Beide sind jetzt in Public Preview verfügbar. Erste Schritte:\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003EInstallieren Sie CoCo für \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-excel-integration\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EExcel\u003C/a\u003E oder CoCo für \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-sheets-integration\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESheets\u003C/a\u003E mithilfe unserer Dokumentation\u003C/li\u003E\n\u003Cli\u003EMelden Sie sich bei Ihrem Snowflake-Konto an, um Ihre erste Analyse zu starten\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003EBleiben Sie dran für weitere Feature-Updates, wie z. B. das Hinzufügen von Tabellenkalkulations-Lineage zum Horizon Catalog, das Exportieren von Visualisierungen und mehr.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EDieser Artikel enthält zukunftsgerichtete Aussagen, unter anderem über künftige Produktangebote. Diese Aussagen stellen keine Garantie dar, dass diese Angebote wirklich bereitgestellt werden. Die tatsächlichen Ergebnisse und Angebote können abweichen und unterliegen bekannten und unbekannten Risiken und Unsicherheiten. Weitere Informationen finden Sie in unserem aktuellen Formular 10-Q.\u003C/i\u003E\u003C/p\u003E\n\u003Chr\u003E\n\n\u003Col data-footnotes=\"true\"\u003E\n\u003Cli id=\"footnote-1\"\u003E Interne Tests über Modelle und Test-Harnesses hinweg wurden zwischen dem 26. Oktober 2026 und dem 28. Oktober 2026 mit Excel für das Web durchgeführt. Die Ergebnisse spiegeln 25 Tabellenkalkulationsaufgaben wider, die mit Opus 5.5, Sonnet 5.5 und Haiku 5.5 in beiden Add-ins in drei Durchläufen ausgeführt wurden; Abbildung 5 zeigt den Median der Sekunden pro Aufgabe über die Durchläufe hinweg. In Abbildung 6 werden Aufgaben, die fehlgeschlagen sind oder ein Timeout aufwiesen, bei der 900-Sekunden-Grenze angezeigt. \u003Ca href=\"#footnote-ref-1\" aria-label=\"Zurück zu Fußnote [1]\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003C/ol\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_ins","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_ins_b","blog_component_18","blog_component_19","blog_component_20","blog_component_21","blog_component_ins_d","blog_component_ins_c","blog_component_22","blog_component_23","blog_component_24","blog_component_25","blog_component_26","blog_component_27"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 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