Run Time solution for reading different days granularity.

Hi jaryszek ,

Yes, your scenario is handled very efficiently at runtime without importing the massive dataset. The solution uses Power BI's DirectLake mode, which relies on a powerful technique called partition pruning. This means Power BI will not scan 10 years of data; it will intelligently read only the data for the two specific days you select.

The mechanism works like this: when a user selects 01.01.2015 and 01.01.2025 in a slicer, Power BI generates a query with a filter for those two dates. The Microsoft Fabric engine receives this query and, thanks to partition pruning, it understands that the data is organized into date-based folders. Instead of scanning every file, the engine intelligently navigates directly to the specific folders corresponding to those two days, completely ignoring the terabytes of data in all other folders. It's like knowing exactly which two folders in a massive filing cabinet to open, rather than searching through every single one.

This entire process hinges on one crucial prerequisite: your data must be correctly structured in the lakehouse. Your Parquet files should be saved using Hive-style partitioning, where the folder path itself contains the date information. A correct path for one of your files would look something like this:

YourLakehouse/Tables/YourTable/year=2015/month=1/day=1/data_file_01.parquet
You mentioned Direct Query, and it's important to know you'll be using something even better: DirectLake mode. This is the default for a Fabric Lakehouse and is much faster ⚡. While Direct Query translates your requests into SQL, DirectLake mode allows the Power BI engine to read the Parquet files directly from OneLake without any translation or intermediate steps. Think of it as reading a book in its original language (DirectLake) versus waiting for a translation (Direct Query).

In essence, as long as your Parquet files are partitioned by date in the Fabric Lakehouse, the system is perfectly designed to handle your request. The combination of proper data structure and DirectLake mode ensures that your reports will be fast and responsive, querying only the tiny slivers of data needed at runtime, no matter how large the total dataset is.

Best regards,