Identifying Toe on Phaseouts/ Closeouts
Hello, I've noticed on all daily reports that the toe type is not listed in the Phaseout or Closeout section. Is there a way to add this important information to the daily reports. When we send out these reports the customer needs to know if it is Steel Toe, Soft Toe or Composite Toe. Thank you! David Loya Key Account Manager Work Group
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.Enabling Keyless Authentication - Workload Identity Federation for GCP from Microsoft data platform
Category: Feature Request / Security & Authentication Impacted Services: Azure Data Factory (BigQuery Linked Service), Power BI (Desktop & Service), Microsoft Fabric (Lakehouse, Dataflows Gen2, Data Pipelines), Power Query Connectors Executive Summary Currently, Microsoft’s Google BigQuery integrations in Azure Data Factory (ADF), Power BI, and Microsoft Fabric rely exclusively on Service Account JSON Keys for machine workloads or interactive User OAuth / Single Sign-On for human users. There is no native support for Workload Identity Federation (WIF) between Microsoft Entra ID and Google Cloud Platform (GCP) for non-interactive machine workloads. In enterprise environments—particularly where Google BigQuery endpoints are publicly accessible—forcing the use of static, long-lived JSON key files presents a severe credential leakage risk, increases management overhead, and violates zero-trust principles. We request Microsoft prioritize adding native Workload Identity Federation (Machine-to-Machine) support across ADF, Power BI Desktop/Service, and Microsoft Fabric (including Lakehouse). Security & Risk Exposure Analysis 1. Data Exfiltration Risk via Public Endpoints: Many enterprise BigQuery instances rely on GCP’s public API endpoints, protected primarily by IAM authentication and network controls. Storing a service account key file creates a single point of failure: if a key with BigQuery Data Viewer or BigQuery Admin roles is leaked, an attacker can query or exfiltrate sensitive data from any public IP address without triggering perimeter network alerts. 2. High Credential Blast Radius: Unlike short-lived OIDC tokens (which expire within 1 hour), GCP Service Account JSON keys remain valid until explicitly revoked. If exposed via build logs, code repositories, unencrypted backups, or compromised storage, the exposure window lasts indefinitely. 3. Operational Overhead & Rotation Risk: Storing keys in Azure Key Vault mitigates storage risk but does not eliminate lifecycle risk. Automated rotation of GCP JSON keys across Key Vault, ADF, Lakehouse shortcuts, and Power BI datasets is fragile and regularly breaks automated data pipelines. Proposed Solution: Universal Machine-to-Machine Federation Establish a trust relationship between GCP Workload Identity Pools and Microsoft Entra ID (Azure AD), allowing Azure workloads to dynamically request short-lived, auto-rotating GCP access tokens via OIDC without ever generating or storing private keys. Requested Functionality by Ecosystem: Azure Data Factory (ADF Linked Service): Add a new option under Authentication Type: Workload Identity Federation / Entra Managed Identity. Allow ADF runtime engines to pass System-Assigned or User-Assigned Managed Identity tokens to GCP’s Security Token Service (STS) to exchange them for temporary Google Service Account tokens. Power BI Ecosystem (Desktop & Service): Desktop: Enable Power Query connection dialogs to accept Entra Service Principal or Managed Identity authentication for BigQuery models. Service: Support Entra Managed Identity / Service Principal credentials under Data Source Credentials in workspace settings to ensure unattended, scheduled background dataset refreshes run without requiring active user sessions. Microsoft Fabric Ecosystem (Lakehouse, Dataflows Gen2, Data Pipelines): Support Entra Managed Identity / Service Principal credentials directly within Fabric Lakehouses when creating shortcuts or ingesting BigQuery tables. Ensure native Workload Identity Federation is built directly into Fabric Data Pipelines and Dataflows Gen2 to secure modern background ETL/ELT workloads. Reference Documentation Power Query Google BigQuery Connector Docs: Microsoft Docs Azure Data Factory Google BigQuery Connector Docs: Microsoft Docs Google Cloud Workload Identity Federation Overview: GCP Docs Fabric - https://learn.microsoft.com/en-us/fabric/data-factory/connector-google-bigquery?source=recommendations Business & Security Benefits Keyless Security Architecture: Completely eliminates long-lived service account key files and manual key rotation overhead across all data environments. Compliance Alignment: Enables enterprise compliance with SOC 2, ISO 27001, and Zero-Trust standards regarding identity management. Reduced Vulnerability Radius: Even if a public BigQuery endpoint is exposed, access relies exclusively on transient OIDC tokens tied strictly to the runtime identity of the ADF pipeline, Lakehouse job, or Power BI refresh engine.
Support Dynamic Reference Lines and Shaded Regions Between Lines
Power BI currently has limited support for dynamic lines beyond constant and trend lines. It also cannot flexibly shade the area between two reference lines. Suggested improvements: Bind reference lines to DAX measures. Shade the area between two reference lines. Configure the color and transparency of each region. Update lines and shaded regions dynamically with filters. Independently format and label each trend line. Manually placed shapes do not respond to filters or axis changes, so they are not a reliable solution for dynamic analysis.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.
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.Better Job Monitoring in Data Factory for Data Engineering
Hello Microsoft Fabric Team, Thank you for creating Microsoft Fabric. I have one suggestion that can improve the Data Factory experience. When we run many pipelines and jobs, it becomes difficult to find failed jobs and understand what went wrong. Better monitoring will help both new and experienced users. My suggestions are: Show the live status of every job. Show start time, end time, and total running time. Show simple error messages that are easy to understand. Add a Retry button for failed jobs. Send email or Teams notifications when a job fails. Add better search and filter options. Show all job logs on one page. Allow users to download job history as a CSV file. Show basic performance tips for slow jobs. Add a simple dashboard with success and failure statistics. These improvements will help users save time, solve problems faster, and manage data pipelines more easily. Thank you for considering this idea.Show exact row-level error details and failed data preview in Fabric Dataflow Gen2 & Pipeline failur
Right now when a Fabric Pipeline or Dataflow Gen2 fails, we only get generic error messages like: "Data conversion error", "Column not found", "Expression.Error" To find the root cause, we have to manually check lakhs of rows. This wastes 1-3 hours every time for every Fabric developer. What we need: Please add detailed error diagnostics that include: 1. Exact location: Pipeline Activity Name > Dataflow Name > Step Name > Column Name > Row Number 2. Failed value: What was the actual bad value that caused the error 3. Sample data preview: Show top 10 failed rows directly in the error details pane, with option to download as CSV 4. Suggested fix: AI suggestion like "Column 'Amount' has text 'N/A'. Consider replacing with 0 or null" Why this matters: This is the #1 time-wasting issue in Fabric today. In ADF and SSIS we had better debugging. For Fabric to be adopted by enterprises, debugging must be 10x faster. This will save thousands of hours across all Fabric users every week.
