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
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