When it comes to building efficient and reliable Power BI reports, the difference between an average solution and a truly scalable, future-proofed one often lies in how well your Power Query transformations are designed. Power BI is a powerful tool for turning raw numbers into meaningful insights, but without a structured approach to data preparation and query optimisation, reports can quickly become slow, unwieldy, and difficult to maintain.
At Zenzero, we don’t just deliver Power BI migration services – we ensure that organisations fully maximise their investment in the platform. Whether you’re moving from legacy reporting solutions or scaling up existing deployments, our expertise in Power Query best practices helps you streamline the data transformation process, build robust models, and create reports that support confident decision-making.
Why Power Query matters in Power BI
The Power Query Editor sits at the heart of Power BI Desktop, acting as the staging area where you clean, shape, and prepare your source data before it becomes part of your Power BI model. It is where raw data from structured data sources, such as a SQL Server database or relational data source, is transformed into the semantic model that underpins your dashboards.
Handled well, Power Query offers the ability to:
- Import only the relevant data, avoiding unnecessary load on large datasets.
- Apply transformations such as renaming columns, creating custom functions, or consolidating multiple steps into a clear sequence of query steps.
- Optimise performance through techniques like query folding.
Handled poorly, however, inefficient queries can cause slow refresh times, introduce errors, or pull in all the data from large SQL tables when only a subset is required.
Best practices for Power Query in Power BI
Start with the right data source
Always begin by considering the data connectors available in Power BI Desktop. For example, the SQL Server connector provides better performance and flexibility than an ODBC connector. Choosing the right connection ensures you can take advantage of query folding, improve overall query performance, and simplify the way you transform data during preparation.
At Zenzero, we often see businesses relying on generic connectors when a dedicated option is available. This not only limits optimisation but also creates issues with query diagnostics, slows refresh times, and makes the data preview process less reliable.
Promote query folding wherever possible
Query folding is the process where transformations in Power Query are translated into native SQL queries and pushed back to the database. This allows the SQL Server database (or other relational data source) to do the heavy lifting, rather than Power BI.
Best practice:
- Apply filters early in your transformation process to reduce the data being retrieved.
- Use operations that promote query folding (e.g., filtering rows, removing unnecessary data, selecting only the columns you need).
- Avoid steps that break query folding, such as complex text manipulations, row-level calculations, or merging queries in non-optimal ways.
Breaking folding too early can result in pulling the entire dataset into memory and performing transformations locally, which slows down query performance dramatically.
Keep data types correct from the start
Incorrect or inconsistent data types can lead to errors, slow refreshes, and misleading results. Always define the correct data types – whether it’s a date column, number, or text – at the earliest stage of your query steps.
Where possible, let the source data handle type conversions. For example, if a SQL query can cast a column to the right type, do it there instead of in Power Query.
Only import the data you need
One of the most common mistakes we see is loading all the queries and all the data, even if much of it isn’t used in the Power BI model. This not only bloats your dataset but also makes future changes harder to manage.
Best practice:
- Retrieve only a subset of data that is relevant to the report.
- Avoid pulling in raw data you don’t intend to use.
- Use parameters and filters to dynamically control query paths and parameter values.
This ensures your model remains lightweight, efficient, and easier to maintain.
Consolidate multiple steps
Every transformation step adds overhead. While Power Query offers flexibility with multiple queries and multiple transformations, unnecessary query steps can lead to inefficient queries.
Where possible:
- Combine related transformations into fewer, consolidated steps.
- Use the Advanced Editor to streamline M code.
- Create groups and organise the queries pane for better readability.
Adding code comments in your M scripts is also a best practice for future proofing queries, especially in environments where existing queries are reused across other queries.
Use parameters and custom functions wisely
Parameters make your queries flexible, allowing you to control data source settings or query paths dynamically. Similarly, custom functions let you reuse logic across multiple sources or datasets.
But they should be used with care:
- Overusing functions can introduce expensive operations that harm performance.
- Ensure parameter values are clear and well-documented.
Leverage query diagnostics
Power BI includes tools for query diagnostics, enabling you to see where time is spent during the transformation process. This is essential for identifying inefficient queries or bottlenecks caused by multiple transformations.
At Zenzero, we regularly use diagnostics when troubleshooting reports for clients. It allows us to pinpoint whether slowdowns are due to query code, the SQL Server database, or how Power Query optimisation has been applied.
Think about the Power BI model
What happens in Power Query directly affects the Power BI model and ultimately the end-user experience. For instance:
- Avoid calculated columns in the model if they can be handled in Power Query.
- Remove unnecessary data before it reaches the model.
- Structure the data in a way that supports efficient Data Analysis Expressions (DAX).
The goal is a lean, well-structured Power BI model that enhances overall query performance and simplifies maintenance.
Future proof your queries
When creating Power BI reports, think about longevity:
- Name queries and columns clearly.
- Add code comments to explain query steps.
- Structure queries logically so colleagues can understand and extend them.
- Avoid hard-coding parameter values or paths where possible.
This ensures your reports remain robust and adaptable, even as business requirements evolve.
Bringing it all together
The transformation process in Power BI Desktop should never be about pulling in all the data and fixing it later. Instead, it’s about thoughtful data preparation, working with structured data sources, and applying Power Query best practices to ensure your reports perform reliably at scale.
At Zenzero, we understand that every organisation’s source data and reporting needs are unique. That’s why we provide tailored support – whether you need to optimise large SQL tables, design a scalable semantic model, or train your team on optimising Power Query.
Final thoughts
Power BI is one of the most versatile BI desktop solutions available, but its true strength lies in how effectively you use Power Query. By following these best practices – from promoting query folding and preventing unnecessary data, to consolidating multiple steps and preparing a clean data model – you can create reports that scale effortlessly with your business.
Investing time in Power Query optimisation today means fewer performance headaches tomorrow. And with expert guidance from Zenzero, your organisation can ensure that its reporting solutions are not only powerful but also future proof. Get in touch with us today!Â