🏠 홈
벤치마크
📊 모든 벤치마크 🦖 공룡 v1 🦖 공룡 v2 ✅ 할 일 목록 앱 🎨 창의적인 자유 페이지 🎯 FSACB - 궁극의 쇼케이스 🌍 번역 벤치마크
모델
🏆 톱 10 모델 🆓 무료 모델 📋 모든 모델 ⚙️ 킬로 코드 모드
리소스
💬 프롬프트 라이브러리 📖 AI 용어 사전 🔗 유용한 링크
Avancé

Data Engineering ETL Pipelines

#data-engineering #etl #airflow #spark #dbt

Conçoit des pipelines ETL/ELT scalables avec Airflow, dbt ou Spark.

Tu es un expert en Data Engineering. Je veux construire des pipelines de données pour [SOURCE VERS DESTINATION]. Pipelines ETL/ELT complets: 1. **Data Ingestion** : Batch vs streaming, change data capture, API connectors 2. **Data Transformation** : SQL transformations, Python/Spark jobs, dbt models 3. **Orchestration** : Apache Airflow DAGs, Prefect flows, Luigi pipelines 4. **Data Quality** : Validation rules, anomaly detection, data profiling 5. **Storage Architecture** : Data lakehouse, Delta Lake, Iceberg, Hudi 6. **Processing Frameworks** : Apache Spark, Flink, Beam for distributed processing 7. **Monitoring & Alerting** : Pipeline health checks, SLA monitoring, failure alerts 8. **Schema Management** : Schema evolution, data contracts, versioning 9. **Security & Governance** : Data encryption, access controls, data lineage 10. **Cost Optimization** : Resource allocation, spot instances, auto-scaling Fournis les configurations Airflow/dbt, les scripts Spark, les schémas de données et les dashboards de monitoring.