[{"data":1,"prerenderedAt":711},["ShallowReactive",2],{"/de-de/blog/how-we-built-a-structured-streamlit-application-framework-in-snowflake":3,"navigation-de-de":35,"banner-de-de":454,"footer-de-de":467,"Radovan Bacovic":676,"footer-source-/de-de/blog/how-we-built-a-structured-streamlit-application-framework-in-snowflake/":689,"next-steps-de-de":696},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":12,"config":25,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/de-de/blog/how-we-built-a-structured-streamlit-application-framework-in-snowflake","blog",false,"",{"config":9,"title":10,"ogTitle":10,"description":11},{"noIndex":6},"Streamlit-Framework: Systematische Infrastruktur-Governance","Das GitLab Data Team zeigt, wie frühzeitige Infrastruktur-Governance Wartungskosten verhindert. Systematischer Ansatz für Streamlit in regulierten Umgebungen.",{"title":10,"description":13,"authors":14,"heroImage":16,"date":17,"body":18,"category":19,"tags":20},"Das GitLab Data Team zeigt, wie frühzeitige Infrastruktur-Governance exponentiell steigende Wartungskosten verhindert. Ein systematischer Ansatz für Streamlit-Anwendungen in regulierten Umgebungen.",[15],"Radovan Bacovic","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097447/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%284%29_3LZkiDjHLjhqEkvOvBsVKp_1750097447404.png","2025-10-20","Das GitLab Data Team hat dutzende isolierte [Streamlit](https://streamlit.io/)-Anwendungen in eine einheitliche, sichere und skalierbare Lösung für die Snowflake-Umgebung überführt. Die Kombination von Python, Snowflake, Streamlit und GitLab ermöglicht systematische Infrastrukturkontrolle. Dieser Beitrag beschreibt den methodischen Ansatz und die erzielten Ergebnisse.\n\n## Die Ausgangssituation\n\nDutzende Streamlit-Anwendungen in verschiedenen Umgebungen, unterschiedliche Python-Versionen, inkonsistente Sicherheitspraktiken beim Zugriff auf sensible Daten. Manche Anwendungen funktionieren, andere versagen ohne nachvollziehbaren Grund. Niemand weiß, wer welche Anwendung erstellt hat oder wie sie zu warten ist.\n\nGenau vor dieser Situation stand unser Data Team. Anwendungen entstanden isoliert, ohne Standardisierung, ohne Security-Oversight, ohne klaren Deployment-Prozess. Das Ergebnis: ein Compliance-Risiko und eine Wartungslast, die exponentiell wuchs.\n\n**Für Unternehmen mit regulatorischen Anforderungen (DSGVO, Branchenstandards) stellt diese dezentrale Infrastruktur ein systematisches Governance-Problem dar.** Audit-Trails fehlen, Zugriffskontrollen sind inkonsistent, und nachträgliche Compliance-Implementierung verursacht erhebliche Kosten. Der hier beschriebene Ansatz zeigt, wie frühe Governance-Implementierung diese Risiken vermeidet.\n\n![Funktionale Architektur (High-Level-Übersicht)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035999/i50lpkrwy9bok056rdak.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Funktionale Architektur (High-Level-Übersicht)\u003C/i>\u003C/center>\n\n## Der methodische Ansatz\n\nAls \"Customer Zero\" haben wir das gesamte Framework auf GitLabs eigener CI/CD-Infrastruktur und den Projekt-Management-Tools aufgebaut. Die Grundkomponenten:\n\n1. [GitLab](https://about.gitlab.com/platform/) (Produkt)\n\n1. [Snowflake](https://about.gitlab.com/platform/) – Single Source of Truth (SSOT) für Data-Warehouse-Aktivitäten\n\n1. [Streamlit](https://streamlit.io/) – Open-Source-Tool für visuelle Anwendungen mit Python-Code\n\nDies ermöglichte direkten Zugriff auf Enterprise-DevSecOps-Funktionen: automatisierte Tests, Code-Review-Prozesse und Deployment-Pipelines von Beginn an. Durch GitLabs integrierte Features für Issue-Tracking, Merge-Requests und automatisierte Deployments (CI/CD-Pipelines) konnten wir schnell iterieren und das Framework gegen reale Enterprise-Anforderungen validieren. Dieser Internal-First-Ansatz stellte sicher, dass die Lösung im Produktiveinsatz bei GitLab selbst validiert wurde.\n\n### Erkenntnisse aus der Implementierung\n\nDie wichtigste Erkenntnis beim Aufbau des Streamlit Application Framework in Snowflake: **Struktur schlägt Chaos systematisch** – Governance früh implementieren, nicht nachträglich, wenn die Wartungskosten exponentiell steigen.\n\nRollen und Verantwortlichkeiten müssen klar definiert sein. Infrastruktur-Concerns werden von Application Development getrennt, sodass jedes Team sich auf seine Stärken konzentrieren kann.\n\nSecurity und Compliance können keine Nachgedanken sein. Sie müssen von Tag eins in Templates und automatisierte Prozesse integriert werden. Konsistente Standards vorab durchzusetzen ist wesentlich effizienter als nachträgliche Implementierung. Investitionen in Automatisierung und CI/CD-Pipelines zahlen sich aus, da manuelle Prozesse nicht skalieren und menschliche Fehler einführen.\n\n![Framework-Architektur (Gesamtübersicht)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035998/qt9gfemxjnj8kjumkuh7.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Framework-Architektur (Gesamtübersicht)\u003C/i>\u003C/center>\n\n## Das Streamlit Application Framework\n\nDas Framework verwandelt dezentrale Ansätze in eine strukturierte Lösung. Entwicklungsteams erhalten Freiheit innerhalb sicherer Leitplanken, während Deployment automatisiert und Wartungskomplexität eliminiert wird.\n\n### Drei Rollen, ein einheitlicher Prozess\n\nDas Framework führt einen strukturierten Ansatz mit drei klar getrennten Rollen ein:\n\n1. **Maintainers** (Data-Team-Mitglieder und Contributors) verwalten die Infrastruktur: CI/CD-Pipelines, Security-Templates und Compliance-Regeln. Sie stellen sicher, dass das Framework funktioniert und sicher bleibt.\n\n2. **Creators** (Anwendungsentwicklungsteams) konzentrieren sich auf ihre Kernkompetenzen: Visualisierungen erstellen, Snowflake-Daten einbinden, User Experiences gestalten. Volle Flexibilität beim Erstellen neuer Anwendungen von Grund auf, beim Hinzufügen neuer Pages zu bestehenden Apps, beim Integrieren zusätzlicher Python-Libraries und beim Bauen komplexer Datenvisualisierungen, ohne Beschäftigung mit Deployment-Pipelines oder Security-Konfigurationen.\n\n3. **Viewers** (Endnutzer) greifen auf fertige, sichere Anwendungen zu, ohne technischen Overhead. Benötigt wird lediglich Snowflake-Zugriff.\n\n![Rollen-Übersicht und ihre Funktionen](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035999/oatqyx3ug7vsgzishpma.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Rollen-Übersicht und ihre Funktionen\u003C/i>\u003C/center>\n\n## Vollständige Automatisierung\n\nDurch CI/CD-Implementierung gehören tagelange manuelle Deployments und Konfigurationsaufwand der Vergangenheit an. Das Framework bietet:\n\n- **Umgebungsvorbereitung per Kommando:** Mit `make`-Befehlen ist die Umgebung in wenigen Sekunden installiert und einsatzbereit.\n\n```yaml\n================================================================================\n✅ Snowflake CLI successfully installed and configured!\nConnection: gitlab_streamlit\nUser: YOU@GITLAB.COM\nAccount: gitlab\n================================================================================\nUsing virtualenv: /Users/YOU/repos/streamlit/.venv\n📚 Installing project dependencies...\nInstalling dependencies from lock file\nNo dependencies to install or update\n✅ Streamlit environment prepared!\n```\n\n- **Automatisierte CI/CD-Pipelines:** Übernehmen Testing, Code-Review und Deployment von Development bis Production.\n\n- **Sichere Sandbox-Umgebungen:** Ermöglichen sichere Entwicklung und Tests vor Production-Deployment.\n\n```yaml\n╰─$ make streamlit-rules\n🔍 Running Streamlit compliance check...\n================================================================================\nCODE COMPLIANCE REPORT\n================================================================================\nGenerated: 2025-07-09 14:01:16\nFiles checked: 1\n\nSUMMARY:\n✅ Passed: 1\n❌ Failed: 0\nSuccess Rate: 100.0%\n\nAPPLICATION COMPLIANCE SUMMARY:\n📱 Total Applications Checked: 1\n⚠️ Applications with Issues: 0\n📊 File Compliance Rate: 100.0%\n\nDETAILED RESULTS BY APPLICATION:\n...\n```\n\n- **Template-basierte Anwendungserstellung:** Gewährleistet Konsistenz über alle Anwendungen und Pages hinweg.\n\n```yaml\n╰─$ make streamlit-new-page STREAMLIT_APP=sales_dashboard STREAMLIT_PAGE_NAME=analytics\n📝 Generating new Streamlit page: analytics for app: sales_dashboard\n📃 Create new page from template:\nPage name: analytics\nApp directory: sales_dashboard\nTemplate path: page_template.py\n✅ Successfully created 'analytics.py' in 'sales_dashboard' directory from template\n```\n\n- **Poetry-basiertes Dependency-Management:** Verhindert Versionskonflikte und erhält saubere Umgebungen.\n\n- **Organisierte Projektstruktur:** Dedizierte Ordner für Anwendungen, Templates, Compliance-Regeln und Configuration-Management.\n\n```yaml\n├── src/\n│   ├── applications/     # Ordner für Streamlit-Anwendungen\n│   │   ├── main_app/     # Main-Dashboard-Anwendung\n│   │   ├── components/   # Gemeinsam genutzte Komponenten\n│   │   └── \u003Cyour_apps>/  # Eigene Anwendungen\n│   │   └── \u003Cyour_apps2>/ # Weitere Anwendungen\n│   ├── templates/        # Anwendungs- und Page-Templates\n│   ├── compliance/       # Compliance-Regeln und -Checks\n│   └── setup/            # Setup- und Konfigurations-Utilities\n├── tests/                # Test-Dateien\n├── config.yml            # Umgebungskonfiguration\n├── Makefile              # Build- und Deployment-Automatisierung\n└── README.md             # Haupt-README-Datei\n```\n\n- **Optimierter Workflow:** Von lokaler Entwicklung über Test-Schema bis Production, vollständig automatisiert durch GitLab CI/CD-Pipelines.\n\n![GitLab CI/CD-Pipelines für vollständige Prozessautomatisierung](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035998/usyma2jkgiazu9iay1au.png)\n\n\u003Cp>\u003C/p>\n\u003Ccenter>\u003Ci>GitLab CI/CD-Pipelines für vollständige Prozessautomatisierung\u003C/i>\u003C/center>\n\n## Security und Compliance by Design\n\nStatt Security nachträglich hinzuzufügen, baut das Streamlit Application Framework sie von Grund auf ein. Jede Anwendung folgt denselben Security-Standards, Compliance-Anforderungen werden automatisch durchgesetzt. Audit-Trails werden über den gesamten Development-Lifecycle gepflegt.\n\nCompliance-Regeln werden mit einem einzigen Kommando eingeführt und verifiziert. Beispielsweise lassen sich definieren, welche Klassen und Methoden verpflichtend sind, welche Dateien vorhanden sein müssen und welche Rollen für das Teilen der Anwendung erlaubt oder verboten sind. Die Regeln sind flexibel und beschreibend – Definition erfolgt in einer YAML-Datei:\n\n```yaml\nclass_rules:\n  - name: \"Inherit code for the page from GitLabDataStreamlitInit\"\n    description: \"All Streamlit apps must inherit from GitLabDataStreamlitInit\"\n    severity: \"error\"\n    required: true\n    class_name: \"*\"\n    required_base_classes:\n      - \"GitLabDataStreamlitInit\"\n    required_methods:\n      - \"__init__\"\n      - \"set_page_layout\"\n      - \"setup_ui\"\n      - \"run\"\n\nfunction_rules:\n  - name: \"Main function required\"\n    description: \"Must have a main() function\"\n    severity: \"error\"\n    required: true\n    function_name: \"main\"\n\nimport_rules:\n  - name: \"Import GitLabDataStreamlitInit\"\n    description: \"Must import the mandatory base class\"\n    severity: \"error\"\n    required: true\n    module_name: \"gitlab_data_streamlit_init\"\n    required_items:\n      - \"GitLabDataStreamlitInit\"\n  - name: \"Import streamlit\"\n    description: \"Must import streamlit library\"\n    severity: \"error\"\n    required: true\n    module_name: \"streamlit\"\n\nfile_rules:\n  - name: \"Snowflake configuration required (snowflake.yml)\"\n    description: \"Each application must have a snowflake.yml configuration file\"\n    severity: \"error\"\n    required: true\n    file_pattern: \"**/applications/**/snowflake.yml\"\n    base_path: \"\"\n  - name: \"Snowflake environment required (environment.yml)\"\n    description: \"Each application must have a environment.yml configuration file\"\n    severity: \"error\"\n    required: true\n    file_pattern: \"**/applications/**/environment.yml\"\n    base_path: \"\"\n  - name: \"Share specification required (share.yml)\"\n    description: \"Each application must have a share.yml file\"\n    severity: \"warning\"\n    required: true\n    file_pattern: \"**/applications/**/share.yml\"\n    base_path: \"\"\n  - name: \"README.md required (README.md)\"\n    description: \"Each application should have a README.md file with a proper documentation\"\n    severity: \"error\"\n    required: true\n    file_pattern: \"**/applications/**/README.md\"\n    base_path: \"\"\n  - name: \"Starting point recommended (dashboard.py)\"\n    description: \"Each application must have a dashboard.py as a starting point\"\n    severity: \"warning\"\n    required: true\n    file_pattern: \"**/applications/**/dashboard.py\"\n    base_path: \"\"\n\nsql_rules:\n  - name: \"SQL files must contain only SELECT statements\"\n    description: \"SQL files and SQL code in other files should only contain SELECT statements for data safety\"\n    severity: \"error\"\n    required: true\n    file_extensions: [\".sql\", \".py\"]\n    select_only: true\n    forbidden_statements:\n      - ....\n    case_sensitive: false\n  - name: \"SQL queries should include proper SELECT statements\"\n    description: \"When SQL is present, it should contain proper SELECT statements\"\n    severity: \"warning\"\n    required: false\n    file_extensions: [\".sql\", \".py\"]\n    required_statements:\n      - \"SELECT\"\n    case_sensitive: false\n\nshare_rules:\n  - name: \"Valid functional roles in share.yml\"\n    description: \"Share.yml files must contain only valid functional roles from the approved list\"\n    severity: \"error\"\n    required: true\n    file_pattern: \"**/applications/**/share.yml\"\n    valid_roles:\n      - ...\n    safe_data_roles:\n      - ...\n  - name: \"Share.yml file format validation\"\n    description: \"Share.yml files must follow the correct YAML format structure\"\n    severity: \"error\"\n    required: true\n    file_pattern: \"**/applications/**/share.yml\"\n    required_keys:\n      - \"share\"\n    min_roles: 1\n    max_roles: 10\n```\n\nMit einem einzigen Kommando:\n\n```bash\n╰─$ make streamlit-rules\n```\n\nlassen sich alle erstellten Regeln verifizieren und validieren, dass Entwicklungsteams (die eine Streamlit-Anwendung erstellen) die von den Creators (die Policies und Building Blocks des Frameworks festlegen) spezifizierten Richtlinien befolgen und alle Building Blocks an der richtigen Stelle sind. Dies gewährleistet konsistentes Verhalten über alle Streamlit-Anwendungen hinweg.\n\n```yaml\n🔍 Running Streamlit compliance check...\n================================================================================\nCODE COMPLIANCE REPORT\n================================================================================\nGenerated: 2025-08-18 17:05:12\nFiles checked: 4\n\nSUMMARY:\n✅ Passed: 4\n❌ Failed: 0\nSuccess Rate: 100.0%\n\nAPPLICATION COMPLIANCE SUMMARY:\n📱 Total Applications Checked: 1\n⚠️ Applications with Issues: 0\n📊 File Compliance Rate: 100.0%\n\nDETAILED RESULTS BY APPLICATION:\n================================================================================\n✅ PASS APPLICATION: main_app\n------------------------------------------------------------\n📁 FILES ANALYZED (4):\n✅ dashboard.py\n📦 Classes: SnowflakeConnectionTester\n🔧 Functions: main\n📥 Imports: os, pwd, gitlab_data_streamlit_init, snowflake.snowpark.exceptions, streamlit\n\n✅ show_streamlit_apps.py\n📦 Classes: ShowStreamlitApps\n🔧 Functions: main\n📥 Imports: pandas, gitlab_data_streamlit_init, snowflake_session, streamlit\n\n✅ available_packages.py\n📦 Classes: AvailablePackages\n🔧 Functions: main\n📥 Imports: pandas, gitlab_data_streamlit_init, streamlit\n\n✅ share.yml\n👥 Share Roles: snowflake_analyst_safe\n\n📄 FILE COMPLIANCE FOR MAIN_APP:\n✅ Required files found:\n✓ snowflake.yml\n✓ environment.yml\n✓ share.yml\n✓ README.md\n✓ dashboard.py\n\nRULES CHECKED:\n----------------------------------------\nClass Rules (1):\n- Inherit code for the page from GitLabDataStreamlitInit (error)\n\nFunction Rules (1):\n- Main function required (error)\n\nImport Rules (2):\n- Import GitLabDataStreamlitInit (error)\n- Import streamlit (error)\n\nFile Rules (5):\n- Snowflake configuration required (snowflake.yml) (error)\n- Snowflake environment required (environment.yml) (error)\n- Share specification required (share.yml) (warning)\n- README.md required (README.md) (error)\n- Starting point recommended (dashboard.py) (warning)\n\nSQL Rules (2):\n- SQL files must contain only SELECT statements (error)\n🗄 SELECT-only mode enabled\n🚨 Forbidden: INSERT, UPDATE, DELETE, DROP, ALTER...\n- SQL queries should include proper SELECT statements (warning)\n\nShare Rules (2):\n- Valid functional roles in share.yml (error)\n👥 Valid roles: 15 roles defined\n🔒 Safe data roles: 11 roles\n- Share.yml file format validation (error)\n------------------------------------------------------------\n✅ Compliance check passed\n-----------------------------------------------------------\n```\n\n## Developer Experience\n\nOb bevorzugte IDE, webbasierte Entwicklungsumgebung oder Snowflake Snowsight – die Experience bleibt konsistent. Das Framework bietet:\n\n- **Template-basierte Entwicklung:** Neue Anwendungen und Pages werden über standardisierte Templates erstellt, was Konsistenz und Best Practices von Tag eins sicherstellt. Keine verstreuten Designs und Elemente mehr.\n\n```yaml\n╰─$ make streamlit-new-app NAME=sales_dashboard\n🔧 Configuration Environment: TEST\n📝 Configuration File: config.yml\n📜 Config Loader Script: ./setup/get_config.sh\n🐍 Python Version: 3.12\n📁 Applications Directory: ./src/applications\n🗄 Database: ...\n📊 Schema: ...\n🏗 Stage: ...\n🏭 Warehouse: ...\n🆕 Creating new Streamlit app: sales_dashboard\nInitialized the new project in ./src/applications/sales_dashboard\n```\n\n- **Poetry Package Management:** Alle Dependencies werden über Poetry verwaltet, was isolierte Umgebungen schafft, die bestehende Python-Setups nicht stören.\n\n```toml\n[tool.poetry]\nname = \"GitLab Data Streamlit\"\nversion = \"0.1.1\"\ndescription = \"GitLab Data Team Streamlit project\"\nauthors = [\"GitLab Data Team \u003C*****@gitlab.com>\"]\nreadme = \"README.md\"\n\n[tool.poetry.dependencies]\npython = \"\u003C3.13,>=3.12\"\nsnowflake-snowpark-python = \"==1.32.0\"\nsnowflake-connector-python = {extras = [\"development\", \"pandas\", \"secure-local-storage\"], version = \"^3.15.0\"}\nstreamlit = \"==1.22.0\"\nwatchdog = \"^6.0.0\"\ntypes-toml = \"^0.10.8.20240310\"\npytest = \"==7.0.0\"\nblack = \"==25.1.0\"\nimportlib-metadata = \"==4.13.0\"\npyyaml = \"==6.0.2\"\npython-qualiter = \"*\"\nruff = \"^0.1.0\"\ntypes-pyyaml = \"^6.0.12.20250516\"\njinja2 = \"==3.1.6\"\n\n[build-system]\nrequires = [\"poetry-core\"]\nbuild-backend = \"poetry.core.masonry.api\"\n```\n\n- **Multi-Page-Application-Support:** Teams können problemlos komplexe Anwendungen mit mehreren Pages erstellen und neue Libraries nach Bedarf hinzufügen. Multi-Page-Anwendungen sind Teil des Frameworks – Fokus liegt auf der Logik, nicht auf Design und Strukturierung.\n\n![Multi-Page-Anwendungsbeispiel (in Snowflake)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035999/at1q2xgmjthkrgju4okm.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Multi-Page-Anwendungsbeispiel (in Snowflake)\u003C/i>\u003C/center>\n\n\u003Cp>\u003C/p>\n\n- **Nahtlose Snowflake-Integration:** Integrierte Konnektoren und Authentication-Handling für sicheren Datenzugriff bieten dieselbe Experience in lokaler Entwicklung und direkt in Snowflake.\n\n```yaml\nmake streamlit-push-test APPLICATION_NAME=sales_dashboard\n📤 Deploying Streamlit app to test environment: sales_dashboard\n...\n------------------------------------------------------------------------------------------------------------\n🔗 Running share command for application: sales_dashboard\nRunning commands to grant shares\n🚀 Executing: snow streamlit share sales_dashboard with SOME_NICE_ROLE\n✅ Command executed successfully\n📊 Execution Summary: 1/1 commands succeeded\n```\n\n- **Umfassendes Makefile:** Alle gängigen Kommandos sind in einfache Makefile-Befehle verpackt, von lokaler Entwicklung über Testing bis Deployment, inklusive CI/CD-Pipelines.\n\n- **Sichere lokale Entwicklung:** Alles läuft in isolierten Poetry-Umgebungen, schützt das System und bietet Production-ähnliche Experiences.\n\n![Konsistente Experience unabhängig von der Umgebung (Beispiel lokale Entwicklung)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760035999/phmubsb34hn2mfefjvqh.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Konsistente Experience unabhängig von der Umgebung (Beispiel lokale Entwicklung)\u003C/i>\u003C/center>\n\n\u003Cp>\u003C/p>\n\n- **Collaboration via Code:** Alle Anwendungen und Komponenten sind in einem Repository zusammengefasst, was der gesamten Organisation ermöglicht, an denselben Ressourcen zu kollaborieren und doppelte Arbeit sowie redundante Setups zu vermeiden.\n\n## Implementierungsschritte\n\nBei ähnlichen Herausforderungen mit verstreuten Streamlit-Anwendungen:\n\n1. **Bestandsaufnahme:** Bestehende Anwendungen inventarisieren und Problembereiche identifizieren.\n\n2. **Rollen definieren:** Maintainer-Verantwortlichkeiten von Creator- und Endnutzer-Anforderungen trennen.\n\n3. **Mit Templates beginnen:** Standardisierte Anwendungs-Templates erstellen, die Security- und Compliance-Anforderungen durchsetzen.\n\n4. **CI/CD implementieren:** Deployment-Pipeline automatisieren, um manuelle Fehler zu reduzieren und Konsistenz sicherzustellen.\n\n![Die in Snowflake deployte Anwendung](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760036003/mzge9s1fhkhnx38y1a3i.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Die in Snowflake deployte Anwendung\u003C/i>\u003C/center>\n\n## Einordnung\n\nDieses Framework behandelt Daten-Anwendungen als vollwertige Komponenten der Enterprise-Architektur.\n\nDurch die Bereitstellung von Struktur ohne Flexibilitätsverlust hat das GitLab Data Team eine Umgebung geschaffen, in der Teams mit minimalen technischen Vorkenntnissen schnell innovieren können, während höchste Security- und Compliance-Standards gewahrt bleiben.\n\n### Ausblick\n\nWir entwickeln das Framework basierend auf User-Feedback und entstehenden Anforderungen kontinuierlich weiter. Zukünftige Verbesserungen umfassen erweiterte Template-Libraries, verbesserte Monitoring-Funktionen, mehr Flexibilität und eine optimierte User Experience.\n\n**Das Ziel: ein Foundation schaffen, das mit den wachsenden Data-Application-Anforderungen der Organisation skaliert.**\n\nWeitere technische Details zur Implementierung im [englischen Original](https://about.gitlab.com/blog/how-we-built-a-structured-streamlit-application-framework-in-snowflake/).\n\n## Zusammenfassung\n\nDas [GitLab Data Team](https://handbook.gitlab.com/handbook/enterprise-data/) hat dutzende verstreute, unsichere Streamlit-Anwendungen ohne Standardisierung in ein einheitliches, Enterprise-taugliches Framework mit klarer Rollentrennung überführt:\n\n1. **Maintainers** verwalten Infrastruktur und Security.\n\n2. **Creators** konzentrieren sich auf Anwendungsentwicklung ohne Deployment-Overhead.\n\n3. **Viewers** greifen auf fertige, compliance-konforme Apps zu.\n\nDie verwendeten Building Blocks:\n\n1. 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