Column value masking for OLS
When applying Object-Level Security (OLS) in Power BI using columnPermission = none, the protected column is completely unavailable to the user. As a result, if that column is used in an existing visual, the visual may break or display an error. It would be useful to have an additional column-level permission that masks or suppresses the column values without removing the column itself from the semantic model. This way if the column (or measure) is available in a visual it would not break but simply it's value will not be visible for roles who do not have permissions on the column. This way there is no need to make separate visuals (or separate report) for every role. Any measure built using such column (or measure) should carry this permission rendering the resultant measure as masked. This would be similar to the OMIT functionality available in Qlik, where sensitive values can be hidden without negatively affecting the overall user experience.
notebookutils.runtime.context['userName'] doesn't work for Workspace Identity or Service Principal
Please make notebookutils.runtime.context return the name of the Workspace Identity or Service Principal when running a notebook under such identities. userId is populated with the object id of the identity. But the userName is empty - please fix.
Pipeline CI/CD Improvement, Get Item ID activity from workspace ID and item name
Currently when following any branch strategy that requires a separate ephemeral feature (or other) workspace running a notebook from the same workspace requires too much ground work To find the ID of the notebook you need a way to call the APIs to GetItems in the workspace to find the correct ID of the notebook (or other item like the lakehouse), this is overly convoluted requiring API calls through web activity or separate notebook in a stand alone workspace with fixed ID that returns the item IDs in the pipeline's workspace Idea: add a pipeline activity that retrieves the item ID of an item using a workspace ID and item name or a function in pipeline expressions that allows the same to be done so this can be inlined without a dedicated pipeline activity.
Increase column limit of 1024 on sql analytic endpoint and warehouse
Currently there is 1024 max column limit in SQL analytic endpoint and warehouse fabric items. We can view delta lake tables in lakehouse but causes issues with table not syncing to SQL analytic Endpoint. I see the following error message when trying to sync the in SQL analytic endpoint. Corrective Action: Try to split the dataset into multiple tables that have up to 1024 columns. Can this be increased since underlying storage delta parquet doesn't carry this limitation?View Items that depend on a Data Connection
Lineage view is great for showing dependencies between items in a workspace, but it sadly doesn't show data connections. My idea is that when you go to the Manage Connections and Gateways page, clicking on a connection should offer you the option to see what pipelines, etc. are using or reference that connection. This would allow users to quickly identify and remove orphaned connections that may have been created temporarily as part of a proof of concept, or some experimentation.
Notify Subscription Owners About Invalid Email Recipients in Power BI
Power BI should automatically notify subscription owners whenever one or more recipient email addresses are invalid or undeliverable. Currently, report subscriptions may silently fail for specific recipients without providing clear feedback to the person who created and manages the subscription. A notification should identify which email addresses could not receive the subscription and explain the reason, such as an invalid address, deleted user account, or external recipient restriction. This would allow subscription owners to quickly update the recipient list instead of assuming that reports are being delivered successfully. Providing proactive notifications for failed deliveries would improve reliability, reduce support requests, and ensure that important reports reach their intended audience. It would also enhance the overall user experience by making subscription management more transparent and easier to maintain.Improve Power BI Error Messages with Root Cause Diagnostics
Power BI should provide more accurate and actionable error messages by identifying the actual root cause of a failure instead of displaying generic messages. Today, users may see an error such as, "This may be caused by a capacity or licensing issue," even when the real problem is related to the semantic model, such as invalid relationships, duplicate keys, or data model inconsistencies. These generic messages often lead users to troubleshoot the wrong area, wasting time investigating licensing, capacity, or service availability when the issue actually lies within the data model. Power BI could improve the troubleshooting experience by analyzing the failure and presenting more specific guidance. For example, if the error is caused by duplicate keys, invalid relationships, or model validation issues, the message should clearly indicate this and provide recommendations on how to resolve it. Providing root cause diagnostics would reduce troubleshooting time, improve the user experience, and help both business users and developers identify and fix issues more efficiently.
Add a Bulk Rename Option for Data Factory Pipelines and Activities
Many users create large Data Factory projects with many pipelines and activities. When naming rules change, every pipeline or activity must be renamed one by one. This takes a lot of time and increases the chance of mistakes. I suggest adding a Bulk Rename feature in Microsoft Fabric Data Factory. Users should be able to select multiple pipelines or activities and rename them using a common prefix, suffix, or find-and-replace option. Benefits: Saves time for developers. Keeps project names consistent. Reduces manual work and errors. Makes large projects easier to manage. Improves the overall user experience. This small feature would help beginners and experienced users who work with many Data Factory pipelines every day.
