Power Query: Cut Steps, Boost Speed

Key insights

• Power Query overview: The video shows simple ways to cut unnecessary steps when shaping data in Excel or Power BI.
  It explains why fewer steps speed up refreshes and make queries easier to maintain.
• Column transformation: Transform columns in-place instead of adding new ones and then removing originals.
  This avoids the “add column shuffle” that creates many extra applied steps.
• Add multiple columns at once: Use a two-step approach to create several new columns together instead of repeating the same action for each column.
  That pattern reduces applied steps and keeps the query clean.
• Table.Buffer and previews: Buffer intermediate tables to prevent repeated re-evaluation during heavy operations, and disable background previews while editing.
  Both moves cut unnecessary work during refresh and speed up development.
• Data types and push-down logic: Set final data types near the end and push filters or aggregations to the source when possible.
  Doing so minimizes repeated conversions and moves heavy lifting out of Power Query.
• Advanced Editor and query design: Break large queries into smaller, modular queries, review the Advanced Editor to flatten nested steps, and limit operations to needed columns.
  These habits improve reliability, reduce memory use, and make troubleshooting faster.

Introduction

The YouTube video from Excel Off The Grid offers a concise walkthrough of techniques to cut unnecessary steps in Power Query. In roughly eight minutes, the presenter demonstrates two practical methods and timestamps the segments for easy reference. First, the video covers column transformation to modify data in place, and then it shows how to add multiple columns at once, reducing repetitive steps. Overall, the presentation targets users who want faster refreshes and cleaner query trees without relying on advanced scripting.

Column Transformation

The first method teaches viewers to transform columns in their existing positions instead of adding new ones and removing the old versions. Consequently, this avoids the common "add column shuffle" where each transformation creates extra applied steps that clutter the query. By applying transformations directly, the query keeps a shorter step list and preserves performance, especially when working with many columns.

However, this approach presents tradeoffs worth noting. While fewer steps speed up refresh and make the applied steps pane neater, in-place edits can reduce clarity for later reviewers who want to track the original values. Therefore, teams that prioritize auditability may need to pair this method with clear naming conventions or short comments in the M language to retain traceability.

Add Multiple Columns at Once

The second technique shows how to add any number of columns in only two steps, which simplifies workflows when creating calculated or helper columns. As a result, users can avoid incremental "add column" steps that multiply dependencies and slow recalculation. The video demonstrates an approach that structures these additions programmatically so that many columns appear from a single transformation step.

On the other hand, combining multiple column additions into compact steps can complicate later edits and debugging. In particular, if column logic must change, modifying a bundled transformation requires careful attention to avoid unintended side effects. Thus, while the method saves time and reduces step count, it increases the need for disciplined naming and modular logic to maintain long-term maintainability.

Performance Impact and Tradeoffs

The video emphasizes several performance-oriented recommendations, such as buffering and minimizing repeated operations. For instance, using Table.Buffer prevents repeated evaluations when a table is referenced multiple times, which can significantly improve speed on complex queries. Moreover, the presenter suggests setting data types toward the end of a sequence to avoid repeated type conversion overhead during iterative edits.

Nevertheless, these optimizations carry tradeoffs that practitioners must weigh. Buffering accelerates recalculation but increases memory pressure, so it suits desktop work on smaller datasets more than very large enterprise tables. Likewise, pushing computation to the data source improves efficiency, but it depends on access to source-side query capabilities and may require SQL or database skills that some analysts lack. Therefore, choices about where to optimize often balance speed, resource limits, and team skill sets.

Practical Recommendations and Challenges

The video also outlines practical habits that help sustain tidy and fast queries, such as reviewing the Advanced Editor regularly and breaking complex logic into intermediate queries. Consequently, smaller, modular queries are easier to test and reduce the chance of deep, fragile dependency chains that slow refresh. Additionally, turning off background data preview during heavy edits can prevent unnecessary preview refreshes and improve responsiveness while authoring.

Still, these practices introduce real challenges in collaborative environments. For example, modularizing queries requires coordination so that naming conventions and query boundaries remain consistent across teammates. Furthermore, optimizing for refresh performance sometimes conflicts with the need for readability and audit trails, which organizations must weigh when documenting their ETL processes. In short, sensible defaults and team agreements help reconcile performance goals with governance needs.

Conclusion

In summary, the Excel Off The Grid video offers clear, actionable ways to reduce steps in Power Query by transforming columns in place and adding multiple columns in fewer operations. These techniques generally speed up refreshes, lower resource use, and produce leaner applied-step lists, yet they require tradeoffs around memory usage, maintainability, and team coordination. Therefore, viewers should apply the methods selectively and combine them with strong naming conventions and modular design.

Finally, for those seeking hands-on examples, the video includes a demonstration file that illustrates both methods in practice, making it straightforward to reproduce the steps in your own workbooks. Consequently, adopting these tips can both simplify everyday queries and highlight where more advanced optimizations are appropriate for larger projects.

Keywords

Power Query reduce steps, reduce Power Query steps, Power Query optimization tips, Power Query best practices, Excel Power Query steps reduction, Power Query performance tips, Power BI Power Query tips, M language performance