top of page
  • Writer's pictureAnna Cameron

3 Primary Methods For Connecting Your Data with Power BI & Microsoft Fabric

Author: Anna Cameron

The three primary methods that can be used in Power BI include Import, DirectQuery, and the DirectLake.

Leveraging data for decision making begins with connecting to relevant data. However, what seems straightforward often proves to be complicated and time intensive. Factors like data volume, disparate data repositories across different platforms, and the lack of seamless data integration between different platforms can intensify the complexities that come with harnessing business data.


More so, once data is cleaned, migrated, and accessible, it’s important to determine which type of connection is optimal for the given task. Here’s where Microsoft Fabric comes in.


Microsoft Fabric offers a comprehensive suite of tools to streamline data connectivity and analysis. The selection of an appropriate method starts with understanding each method's strengths and limitations to ensure the best outcomes for diverse analytical scenarios.


The three primary methods that can be used in Power BI include Import, DirectQuery, and the DirectLake. Each method carries advantages and drawbacks, so it’s essential to understand them all to see which aligns with your given analytics task.


Import


Importing data directly into a Power BI file is a relatively quick process that is conducive for smaller datasets. However, efficiency is tempered by its inability to sync real time data.


The import process copies data tables from the lake house or warehouse and saves them in the Power BI file itself. Any modifications to the data source require manual importing of the data to reflect the changes in Power BI.


To mitigate this limitation, scheduled refreshes can be used to ensure that data in the Power BI file is current.


DirectQuery


DirectQuery in Power BI offers a direct connection to the necessary data source which maintains data integrity and almost real-time data synchronization. Despite these advantages, this method encounters a performance trade off in slower analytics, particularly in scenarios with complex DAX calculations.


DirectQuery functions by executing queries in the data source’s semantic model to retrieve data that fits the provided query. For example, a DAX query would be translated to a SQL query format to be run in the data source level. The result of that SQL query is then sent back to Power BI report where it can be used in a KPI visual.


DirectQuery finds its niche in scenarios using large data sets where near real time data is more important that swift analytic processing.


DirectLake


DirectLake is a novel approach to data connection in Power BI. It essentially combines the advantages of import and DirectQuery while mitigating the respective limitations.


By using parquet files sourced from Microsoft OneLake, DirectLake eliminates the need for query translation and bypasses the conventional data import, providing accelerated data access. Moreover, like DirectQuery, DirectLake ensures near real time data synchronization, making it a preferred choice for large data models where data changes frequently.


Conclusion


Using big data for decision making may be complex in business environments, but Power BI offers several techniques to make this possible by connecting through Import, DirectQuery + the DirectLake. Each method has its own advantages and limitations.


Import mode is a relatively quick solution best for smaller data set, but is accompanied with the possibility of data staleness.


On the other hand, DirectQuery and DirectLake are robust options for analysis of large data models where the analytics need to stay synced with changes in the data source. While DirectQuery can be a slightly faster method as it offers direct connectivity, it comes with the expense of slower analytics due to the need to translate queries between the data source and the Power BI file.


DirectLake is a promising alternative that combines the strengths of import and DirectQuery while circumventing the need to translate queries.


Ultimately, choosing the best connection method hinges on the specific requirements and nuances of the analytical task, ensuring that data-driven decisions are both informed and agile in response to dynamic business landscapes.


 

Need help understanding and identifying which method is best for connecting your data with Power BI & Microsoft Fabric? Let’s loop you in. Book your intro call with our data experts today.

17 views

Comments


Commenting has been turned off.
bottom of page