A lot new terms come out Microsoft Fabric went G, one of them is direct lake. When you hear that for the first time, you may have the same response as me :”ain’t that the same thing as direct query” well, no, not really.

Microsoft Fabric’s “Direct Lake” mode in Power BI represents a significant innovation in how data is managed and queried within Microsoft’s ecosystem. It combines the benefits of fast data retrieval and real-time analytics by directly querying data stored in a data lakehouse, specifically within Microsoft’s OneLake. This mode enables the creation of Power BI reports and datasets directly on top of the data stored in Parquet files, bypassing the need for traditional SQL query processing.

Direct Lake and DirectQuery are both data connection strategies used within Microsoft’s ecosystem, such as in Power BI, but they operate differently and are optimized for distinct scenarios, we are also going to add import mode to the comparison, so that you get to see a full picture:

Direct Lake

• Data Source: Direct Lake operates on data stored in a lakehouse, specifically within Microsoft OneLake, accessing data stored in delta tables.
• Performance: It combines the speed of import modes with the real-time update capabilities of DirectQuery, enabling fast data access and near real-time analytics.
• Usage Scenario: Ideal for use cases where both high performance and up-to-date data are critical. It directly reads Parquet files, bypassing traditional SQL-based data retrieval, which can significantly speed up data access.
• Integration: Direct Lake is deeply integrated into Microsoft Fabric, enhancing its capabilities with broader data governance, collaboration, and analytics features provided by the Microsoft ecosystem.

DirectQuery

• Data Source: DirectQuery connects directly to external databases or data sources, querying data in real-time each time a user interacts with a report.
• Performance: While it ensures that the data is always current, DirectQuery can lead to slower performance compared to import modes because every interaction triggers a query to the external database, which then processes and returns the results.
• Usage Scenario: Suited for scenarios where data freshness is paramount and the volume of data does not justify importing it into Power BI for performance reasons. It’s also used when data cannot be replicated due to size or security policies.
• Integration: DirectQuery allows for a high degree of flexibility in connecting to various types of data sources, but it might require additional considerations for performance optimization, such as managing query complexity and understanding the load on the underlying source.

In summary, while both Direct Lake and DirectQuery provide real-time data access, Direct Lake is optimized for speed and efficiency by working within the confines of a data lakehouse, leveraging the latest data without frequent full refreshes. DirectQuery, on the other hand, offers real-time data access directly from the source, which can be critical for certain types of analytical workloads where data outside of a data lakehouse needs to be accessed dynamically.

Now, last but not the least, let’s take a look at import mode.

Import Mode

• Data Storage: In Import mode, data is physically imported into Power BI from the data source and stored within the Power BI dataset.
• Performance: Import mode offers the fastest query performance because the data is loaded into Power BI’s highly optimized, in-memory engine, eliminating the need to fetch data from the source during each query.
• Data Freshness: The data is static after import and needs manual or scheduled refreshes to stay up-to-date. This might not be ideal for real-time scenarios but is suitable for datasets where freshness every few hours or daily is acceptable.
• Usage Scenario: Best for scenarios where the dataset size is manageable within Power BI’s capacity limits and when the highest performance is required for interactive reporting.