Power Query, while a powerful tool for data transformation and cleaning in Excel, does have some limitations to be aware of:

* Data Source Support: Not all data sources are compatible. While Power Query offers a wide range, there might be specific databases or APIs it can't connect to directly. You might need to use custom connectors or workarounds.
* Data Size: While it can handle large datasets, there are limitations.
* 32-bit Excel: Limited to around 1GB of data due to memory restrictions.
* 64-bit Excel: Limited by available virtual memory. For very large datasets, consider using Power BI Desktop which offers better handling of massive data volumes.
* Limited Functionality for Complex Transformations: Power Query excels at cleaning and shaping data, but for highly complex data manipulation or statistical analysis, specialized tools like R or Python might be more suitable.
* Limited Data Visualization: While you can preview data within Power Query Editor, it's not designed for creating complex visualizations. Integrate your transformed data with Excel charts or consider Power BI for advanced visualizations.
* Version Differences: Power Query functionalities and limitations might differ slightly between Excel versions (Windows vs. Mac) or between Excel and Power BI Desktop.
* M Language Learning Curve: While the Power Query interface is user-friendly, for advanced transformations, you might need to learn the M language which has a learning curve.

Additional Considerations:

* Security: Power Query queries can potentially expose sensitive data within the M code. Be mindful of data privacy and avoid embedding sensitive information in your queries.
* Performance: Complex queries with large datasets can impact performance. Consider optimizing your queries for efficiency, especially when working with millions of rows.

Overall, Power Query remains a valuable tool for most Excel users. Understanding its limitations helps you choose the right approach for your data transformation needs.