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What happened Recently I was working on data where I needed to pick one best row per group, then merge that result with a lookup table. Here’s the head-scratcher I hit: the pick looked right in preview, but after the merge some groups showed different rows, like the merge had used the pre-pick data. What was happening is that Power Query re-evaluated and re-ordered things during the merge, which changed which row got selected. The fix was to freeze the picked result with Table.Buffer right after the pick so the merge used exactly those rows. I also made the lookup one row per key to avoid duplicate expands. After that, everything stayed stable on refresh. Why and how Table.Buffer works Why the drift happens Power Query is lazy. It does not materialize intermediate steps until needed. A Merge can push work back to the source (folding). That re-evaluation can change row order. If your “pick one” depends on order, the selected row can change during the Merge. What Table.Buffer...

First and foremost, I want to take a moment to acknowledge Brandon Pires , who originally crafted the script that inspired all of this work. Brandon’s creativity laid the foundation for this blog post, and his efforts are key to helping Power BI developers improve efficiency and cleanliness in their reports. Introduction: Using Templates to Keep Your Dataverse Clean When working with Microsoft Dataverse in Power BI, things can get messy quickly. Dataverse often contains a wealth of fields, system columns, and metadata that can make your reports harder to understand, cluttered, and less efficient. However, by leveraging templates and custom functions, you can automatically clean and format your data, allowing you to focus on deriving insights rather than grappling with unnecessary noise. One such function, inspired by Brandon Pires, is designed to clean column names from Dataverse and keep them tidy. In this blog post, I will explain how this function works, why it improves efficien...

In today’s fast-paced development environments, seamless integration between customer and development tracking systems can be crucial for maintaining transparency, efficiency, and alignment across teams. Our objective centers around a scenario faced by some organizations: synchronizing customer requirements captured by staff in Microsoft Dataverse with the development work tracked in Azure DevOps (ADO) . Business Case and Goals Our service teams interact with customers to capture requirements, which are then stored in Microsoft Dataverse. As development plans are formulated, corresponding ADO items are created for each requirement. The primary goals of our integration efforts are: Visibility for Service Staff: Enable service teams to view the status of the ADO items associated with customer requirements directly within Dataverse. This integration aims to eliminate the need for service staff to navigate away from their primary system to check development progress, fostering a mor...