Resource Cost Indicator for Power Query Transformations

Features of the Proposed Indicator:

1. Visual Cue: A color-coded system (e.g., green, yellow, red) to quickly show the relative expense of a step. Green could indicate a minimal impact, yellow for moderate, and red for high resource consumption.
2. Tooltip Details: Hovering over the indicator could display a tooltip with more detailed metrics, such as estimated execution time, memory consumption, or computational intensity.
3. Historical Data Consideration: The system could use historical performance data to refine its predictions, adapting to the specific machine or environment in which Power BI operates.
4. Configurable: Allow users to configure or toggle this feature, adjusting the sensitivity or even choosing what kind of metrics they prioritize (e.g., some might prioritize speed over memory).
5. Guided Optimization: For steps marked as highly resource-intensive (red), offer suggestions or links to best practices on optimizing such operations.

Benefits:

1. Enhanced User Experience: Users can instantly gauge the cost of their transformation steps, helping them pinpoint bottlenecks or expensive operations.
2. Education: Especially for newer users, this can serve as an educational tool, teaching them about the relative expense of different operations in real-time.
3. Efficient Query Development: By quickly spotting and optimizing costly steps, users can streamline their ETL processes, leading to faster refresh times and smoother report interactions.

In addition to the step-by-step resource cost indicators, I propose the introduction of an "Overall Performance Score" for the entire query in Power Query. This would give users an aggregated view of the query's efficiency, taking into account all individual transformation steps.

Features of the Overall Performance Score:

1. Aggregated Metric: Compute a cumulative score based on the individual indicators of each transformation step. This could be an average or a weighted score, depending on the perceived impact of each step on the entire query.
2. Historical Comparison: Whenever a change is made to the query, the system could display the difference in the score from the previous version (e.g., "Performance improved by 15%"). This provides instant feedback on the effect of the recent modification.
3. Visual Representation: A progress bar or gauge visualization to instantly show how the query performs. For instance, if the bar fills up only 50%, it indicates the query is running at half the optimal efficiency.
4. Recommendations: Alongside the score, the system could provide general optimization recommendations based on the query's performance. For instance, if the score is below a certain threshold, it might suggest checking for unnecessary transformations or pushing some computations back to the data source.

Benefits:

1. Instant Feedback: Users can quickly gauge the impact of their changes, helping them understand if they're moving in the right direction with their optimizations.
2. Goal Setting: With a clear performance score, users can set tangible optimization goals, aiming to achieve a certain score or percentage improvement.
3. Streamlined Decision Making: When deciding between two alternative transformation methods, users can choose the one that results in a better overall performance score.