Incremental Load in Microsoft Fabric | PySpark + Lakehouse + Pipeline (Step-by-Step)
In this video, we build a complete Incremental Data Load pipeline in Microsoft Fabric using PySpark, Lakehouse, and Data Pipeline.
This is a real-world Data Engineering concept where we process only new files instead of reprocessing all data — making the pipeline efficient, scalable, and production-ready.
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🎯 What you’ll learn in this video:
✔ How to implement incremental load in Microsoft Fabric
✔ Creating Delta tables for tracking processed files
✔ Reading files from Lakehouse (Files section)
✔ Extracting file names dynamically
✔ Using left_anti join for incremental filtering
✔ Loading only new data into target table
✔ Maintaining processed files tracking table
✔ Automating using Data Pipeline
✔ Testing incremental behavior
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🧠 Key Concept Explained:
We track processed files using a metadata table and load only new files using left_anti join.
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📂 Architecture Used:
Files → PySpark Notebook → Delta Tables → Tracking Table → Pipeline Automation
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👥 Who should watch this?
• Data Engineers
• Microsoft Fabric learners
• PySpark developers
• ETL pipeline developers
• Interview preparation candidates
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🚀 Why this is important:
Incremental loading is a core Data Engineering concept used in real production pipelines to improve performance and reduce cost.
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💡 What you’ll understand:
• File-level incremental processing
• Delta Lake usage in Fabric
• Notebook-based ETL pipelines
• Automation using pipelines
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