Power BI Embedded

Power BI Embedded is a great tool for ISVs offering a BI product for their customers. Generally, the reports exposed to the customers are personalized to some extent - one can use the Power BI JavaScript library to interact with the reports and tweak the visuals based on the logged-in user. Another way reports differ from user to user is the underlying data that user is allowed to see. This is controlled using Row-level Security. A standard method to implement Row-level security is to pass the user's email address to the data model, and have rules filter the data model appropriately based on the data that user is permitted to see. However, sometimes it can be useful to filter the data model based on more than just a user's email address. This blog indicates how to modify an Embed Request (made by Power BI Embedded) to provide additional context on what data the embedding application would like to be returned for a user to view on a report.

We've done a lot of work at endjin with Azure Analysis Services over the last couple of years - but none of it has been what you'd call "traditional BI". We've pulled, twisted and bent it in all sorts of directions, using it's raw analytical processing power to underpin bespoke analysis products and processes. This post explains some of the common (and not-so-common) reasons why you might want to do similar things, and how Azure Analysis Services might be the key to unlocking your data insights.

A Power BI based solution typically consists of a variety of technologies - for example Azure data platform services containing source data. As such, automation of Power BI resources needs to be considered as part of a wider DevOps strategy. This post describes the specific steps needed in order to fully automate the creation and security of Power BI workspaces using Powershell and Azure DevOps pipelines.