# Direct Lake Incremental Framing Effect

I was writing a blog on Direct Lake incremental framing but my colleague Chris Webb beat me to it and just [published an excellent blog](https://blog.crossjoin.co.uk/2025/08/31/performance-testing-power-bi-direct-lake-models-revisited-ensuring-worst-case-performance/). To summarize, with incremental framing, when a Direct Lake semantic model refreshes, it analyzes the Delta log to see what's changed since the last refresh:

* It identifies which parquet files are new, which have been modified, and which have been removed
    
* For unchanged data, it maintains the existing data in memory (preserving dictionaries and other optimizations)
    
* It only reloads the data from new or modified parquet files
    
* It removes from memory any data from deleted parquet files
    

You can read more about it in the [documentation](https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview#framing).

I will instead highlight another update based on the work by two of my other colleagues, [Phil Seamark](https://dax.tips/) and [Michael Kovalsky](https://www.elegantbi.com/about). Semantic Link Lab’s `.delta_analyzer_history()` function estimates the incremental framing effect based on the updates to the delta table. 0 means no benefit at all and 100% means highly effective. Note that this is based on the changes to the delta table and does not account for any refresh/updates made to the semantic model.

```python
import sempy_labs as labs
labs.delta_analyzer_history("sales").tail(2) #sales is the name of the table in the lakehouse attached to the notebook
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1756745664490/7c5c1625-8e39-4bc6-8297-963dae02f3f1.png align="center")

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1756745772272/13d0c3e4-3948-4ae1-b53e-2dc72ef871ae.png align="center")

## References:

* [microsoft/semantic-link-labs: Early access to new features for Microsoft Fabric's Semantic Link.](https://github.com/microsoft/semantic-link-labs)
    
* [Direct Lake overview - Microsoft Fabric | Microsoft Learn](https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview)
    
* [Performance testing Power BI Direct Lake models revisited: ensuring worst-case performance](https://blog.crossjoin.co.uk/2025/08/31/performance-testing-power-bi-direct-lake-models-revisited-ensuring-worst-case-performance/)
    
* [Chris Webb's BI Blog: Performance Testing Power BI Direct Lake Mode Datasets In Fabric](https://blog.crossjoin.co.uk/2023/07/09/performance-testing-power-bi-direct-lake-mode-datasets-in-fabric/)