Utilizing the Power Query M language for data transformation comes with responsibilities, as there are commonly encountered challenges that one has to bear in mind:
Slow Queries: Developing simple queries by neglecting the optimization components may prolong data refresh times. Therefore, it is necessary to ensure that the queries remain clear and concise.
Understanding the concept of query folding is crucial: It allows the query language to push data processing back to the database, preventing unnecessary data transfer into Power Query.
Be mindful that some transformations can disrupt query folding, leading to performance issues. This knowledge will empower you to make informed decisions and optimize your queries.
Missing Data Types: Declaring inappropriate data types can also lead to incorrect transformations and reports further down in the process.
Inundation with Irrelevant Information: Adding excessive non-essential columns or rows tends to enlarge your model and unnecessarily impact efficiency. Always filter data so that only the precise amount needed is brought in.
Difficult to Understand Encapsulation: Power Query processes may get involved in-depth without any informative notes, so understanding and troubleshooting the processes later on becomes a challenge.
The avoidance of such challenges enables the user to achieve a more desirable result in Power BI, especially regarding performance and the data transformation task.
