Microsoft Fabric introduces Dataflows Gen2, an evolution of Power BI Dataflows, enabling scalable, high-performance ETL (Extract, Transform, Load) pipelines within OneLake. However, working with large datasets requires optimization techniques to minimize refresh times, reduce memory consumption, and enhance query performance.

In this guide, we’ll cover best practices for optimizing Microsoft Fabric Dataflows to ensure faster processing, efficient storage, and lower computational costs.

1. Choose the Right Storage Mode: Direct Lake vs. Import Mode

Direct Lake Mode (Best for Large Data)

  • Eliminates scheduled refreshes by allowing Power BI to query OneLake directly.
  • Provides near-instant query performance without loading data into memory.
  • Reduces data duplication and enhances governance.

Import Mode (For Aggregated & Transformed Data)

  • Loads data into Power BI dataset memory, requiring scheduled refreshes.
  • Recommended for scenarios needing pre-aggregated or denormalized data.

Best Practice: If your organization works with large-scale data (>1TB), use Direct Lake Mode instead of Import Mode to reduce performance bottlenecks.

2. Optimize Query Performance with Incremental Refresh

Instead of reloading the entire dataset during every refresh cycle, incremental refresh updates only new or modified records, significantly improving efficiency.

How to Enable Incremental Refresh

  1. Go to Dataflow Settings → Select Enable Incremental Refresh.
  2. Define Date Ranges:
    • Historical Data: Store 5 years of data.
    • Incremental Data: Refresh only the last 1 month of records.
  3. Use Date Partitioning: Filter tables with DateKey >= StartDate AND DateKey <= EndDate.

🔹 Impact: Reduces data processing time by 90% for large datasets.

3. Use Dataflow Staging for Large ETL Pipelines

Instead of performing all transformations in a single dataflow, use staging layers:

✅ Raw Dataflow: Extract raw data without transformations.
✅ Transformation Dataflow: Apply joins, filtering, and DAX in a separate dataflow.
✅ Final Dataflow: Load the cleaned dataset into Power BI.

🔹 Impact: Reduces memory usage and eliminates redundant recalculations.

4. Optimize DAX Measures for Faster Queries

If you’re using Dataflows with Power BI, inefficient DAX can slow performance. Follow these DAX best practices:

  • Avoid IF statements in large datasets: Replace IF with SWITCH(TRUE(), condition1, result1, ...) for better efficiency.
  • Reduce COUNTROWS in complex queries: Replace COUNTROWS(FILTER(...)) with CALCULATE(COUNT(...)).
  • Minimize high-cardinality columns in Power BI to reduce dataset size.

5. Reduce Data Refresh Load with Query Folding

Query folding pushes transformations back to the data source, reducing load on Fabric’s compute engine.

🔹 How to Check Query Folding:

  • In Power Query, right-click a transformation step → Select View Native Query.
  • If disabled, apply transformations at the SQL level instead.

🔹 Impact: Increases data refresh speed by 50-70% for large SQL-based dataflows.

Conclusion

Optimizing Microsoft Fabric Dataflows is essential for scalability and real-time analytics. By implementing Direct Lake Mode, Incremental Refresh, Query Folding, and optimized DAX, you can drastically reduce query times, refresh durations, and memory usage.