Design a simple ETL pipeline
Design a complete ETL pipeline with error handling, monitoring, and Python code for multi-source data integration.
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Je dois construire un pipeline ETL pour [OBJECTIF] qui extrait des données de [SOURCES] vers [DESTINATION] (ex : Data Warehouse, base SQL, Google BigQuery, fichiers Parquet).
Sources de données :
- [SOURCE_1] : [DESCRIPTION] (ex : API REST, CSV quotidien, base MySQL)
- [SOURCE_2] : [DESCRIPTION] (ex : Google Sheets, Salesforce SFTP)
- [SOURCE_3] : [DESCRIPTION] si applicable
Transformations nécessaires :
- [TRANSFORMATION_1] (ex : dédoublonnage, jointures, agrégations)
- [TRANSFORMATION_2] (ex : standardisation formats, calcul de métriques dérivées)
Contraintes : [CONTRAINTES] (ex : traitement de [VOLUME] lignes/jour, latence max [LATENCE], conformité RGPD)
Conçois le pipeline ETL avec :
- L'architecture globale du pipeline avec schéma de flux de données
- La stratégie d'extraction : full load vs incremental avec gestion des deltas
- Les transformations avec règles de qualité et validation des données
- La gestion des erreurs, alertes et reprise sur panne
- Le monitoring : métriques clés à surveiller (latence, volume, taux d'erreur)
- Le code Python complet avec pandas/SQLAlchemy ou dbt si applicable
- La documentation technique et le calendrier d'exécution (cron)
Stack préférée : [STACK_TECH]
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Why this prompt works
<p>This prompt is effective because it addresses all dimensions of a professional ETL: architecture, data quality, resilience, and monitoring, producing a production-ready solution.</p>
Use Cases
Expected Output
ETL architecture, complete Python code, quality rules, error handling, monitoring, and technical documentation.
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