How to Implement Incremental Load in Microsoft Fabric (Using Dataflow Gen2 & Lakehouse)
Introduction
In modern data engineering, efficiently handling large volumes of data is essential. Performing full data loads repeatedly increases processing time and resource consumption. Incremental loading addresses this challenge by ensuring that only new or modified data is processed and stored.
In this blog, we will walk through how to implement incremental load in Microsoft Fabric using Dataflow Gen2 and a Lakehouse.
What is Incremental Load?
Incremental load is a data ingestion technique where only the changes in data are processed. This includes:
– Newly inserted records
– Updated records
Existing unchanged data is not reprocessed.
Example:
If a dataset contains 10,000 records and 500 new records are added:
– A full load processes all 10,500 records
– An incremental load processes only the 500 new records
Why Incremental Load is Important
– Improves data pipeline performance
– Reduces processing time
– Optimizes compute and storage usage
– Scales efficiently for large datasets
Key Concept: Watermark Column
A watermark column is used to identify newly added or updated records.
Common examples include:
– CreatedDate
– ModifiedDate
– LastUpdated
The incremental logic is typically based on:
ModifiedDate > Last Load Timestamp
Step 1: Create a Lakehouse
1. Open Microsoft Fabric
2. Navigate to your workspace
3. Click New → Lakehouse
4. Provide a name and create it
This Lakehouse will serve as the storage layer for incremental data.
Step 2: Create a Dataflow Gen2
1. In the workspace, click New → Dataflow Gen2
2. Provide a name
3. Open the Power Query Online editor
Step 3: Connect to a Data Source
Dataflow Gen2 supports multiple sources such as SQL Server, Excel or CSV files, APIs, and cloud storage.
Select the required source and load the dataset. Ensure the dataset contains a watermark column.
Step 4: Apply Incremental Filter
Apply a filter using the watermark column:
ModifiedDate > Last Refresh Timestam
Step 5: Configure Destination
1. Click Add Destination
2. Select Lakehouse
3. Choose Append mode
Append mode ensures that only new data is added without overwriting existing records.
Step 6: Publish and Schedule Refresh
1. Click Save
2. Click Publish
3. Configure scheduled refresh
The pipeline will now automatically load incremental data at defined intervals.
Handling Updates (Advanced Scenario)
Load incremental data into a staging table and use merge logic to update the main table.
What Happens Behind the Scenes
– Data is stored in Delta format
– ACID transactions are supported
– Schema enforcement is applied
– Metadata is maintained
– SQL endpoint is available
Common Mistakes to Avoid
– Using Replace instead of Append mode
– Not maintaining the watermark correctly
– Ignoring update handling logic
– Creating duplicate records
Conclusion
Incremental load is a must-have technique for building scalable and efficient data pipelines in Microsoft Fabric.
By combining Dataflow Gen2 and Lakehouse, you can easily:
- Reduce load time
- Optimize performance
- Build production-ready pipelines
Mastering incremental loading will take your data engineering skills to the next level.
