Azure Data Factory Source & Sink Options Explained with Practical Examples ADF Tutorial

Azure Data Factory Source & Sink Options Explained with Practical Examples ADF Tutorial In this video, we explore Azure Data Factory (ADF) Source and Sink settings step by step using a Copy Data activity. Whether you are a beginner or a data engineer, this practical walkthrough will help you understand how to configure Source and Sink datasets to move data between services like Azure Blob Storage, Data Lake, Azure SQL Database, and more. 🔹 What You’ll Learn - Source Tab (Input Data) - Selecting a Source Dataset (e.g., Azure Blob, ADLS Gen2, SQL Server, REST API). - Understanding File Path Type – File path in dataset vs Wildcard vs List of files. - Using Filter by Last Modified and Start/End Time to load only recent files. - Options like Recursively to include subfolders and Partition Discovery for large datasets. Sink Tab (Output Target) - Choosing a Sink Dataset (Azure SQL, Data Lake, Synapse, etc.). - Copy Behavior options: Preserve Hierarchy, Flatten Hierarchy, Merge Files. - Setting Max Concurrent Connections and Block Size for performance tuning. - Adding Metadata, controlling Quote All Text, and defining File Extension (.csv, .parquet, .txt). ✅ Practical Demo Full pipeline example: Copying data from Azure Blob Storage to Azure Data Lake / SQL Database. How each Source/Sink property impacts data movement. Tips to improve speed, reduce cost, and handle large data files. 💡 Key Takeaways Source = where your data comes from (input). Sink = where your data is written (output). Correct configuration ensures reliable, fast, and cost-effective ETL pipelines. #azuredatafactory #azure #azureservices