Azure Data Factory Dynamic Parameters | Reusable Pipelines | Date Partition Tutorial | Data Decoded

In this video, we break down one of the most important Azure Data Factory concepts for building scalable, production-grade pipelines — ADF Parameterization. Learn how to create dynamic pipelines using: ✅ Pipeline Parameters ✅ Dataset Parameters ✅ Dynamic Expressions ✅ Date Partitions ✅ Reusable Pipelines ✅ Trigger-based Execution Dates If you are still creating separate pipelines for every file, date, or environment — this video will completely change how you design ADF solutions. We’ll cover: 🔹 What are ADF Parameters? 🔹 Why hardcoding is a bad practice 🔹 How to create dynamic file paths 🔹 How to load any date using parameters 🔹 Difference between utcnow() and trigger().scheduledTime 🔹 Real-world production use cases 🔹 Common mistakes engineers make in ADF 🔹 Best practices for scalable Azure Data Engineering pipelines Links - Excalidraw - https://excalidraw.com/#json=dc1jEtVRI0Q9ZrI3CvWTl,xSvmHoRPrmqnknxexiHqSg Linkedin - https://www.linkedin.com/in/chirag-sachdeva-data-engineer/ #AzureDataFactory #ADF #DataEngineering #Azure #ETL #DP203 #DataEngineer #AzureTutorial #Pipeline #Parameterization #Databricks #PySpark #DataDecoded