Power Automate Export to PDF Returns Blank Report for Report Connected to Shared Semantic Model
Hi Team, I am facing an issue with Power Automate's "Export To File for Power BI Reports" action. Scenario I have a Power BI Semantic Model (Dataset A). Report A is built directly on Dataset A. Report B is another report built using the same semantic model(shared dataset/thin report approach). Expected Behavior When Power Automate exports Report B to PDF, the report should contain the same data that is visible in Power BI Service. Actual Behavior Exporting Report A through Power Automate generates a PDF with data correctly displayed. Exporting Report B through Power Automate generates a PDF, but the visuals are blank and no data is shown. There are no export errors. Additional Findings Manual export from Power BI Service (File > Export > PDF) works correctly for both reports and the generated PDF contains data. The issue only occurs when exporting through Power Automate. Both reports use the same semantic model. The semantic model is accessible and contains data. If I export pages from Report A, data is visible in the PDF. If I export pages from Report B (connected report/thin report), the PDF is blank. Questions Does the Export To File for Power BI Reports action have any limitations with thin reports or reports connected to a shared semantic model? Are there any permission requirements (Build permission, RLS, semantic model access, etc.) that differ between manual export and Power Automate export? Has anyone experienced blank PDF exports when using a report connected to an existing semantic model while manual exports continue to work? Any guidance would be appreciated. Thank you.
How to design a metadata-driven framework to run 600+ Fabric notebooks with parallel execution?
Hi Fabric Community, We are currently planning to migrate 600+ notebooks from Azure Databricks to Microsoft Fabric. Our current requirement is to build a metadata-driven orchestration framework using: Fabric Data Pipelines for orchestration SQL Database for metadata/control tables Fabric Notebooks for data processing OneLake/Lakehouse as the target storage Instead of creating separate pipelines for each notebook, we would like to have a generic metadata-driven pipeline that dynamically reads notebook information from SQL Database and executes the required notebooks. For example, our metadata table could contain: Notebook ID Notebook name/path Priority Dependency Parameters Data volume Expected runtime Compute/pool requirement Retry count Active/inactive flag Our main challenge We may need to execute approximately 40 notebooks in parallel. We would like to understand the recommended Fabric architecture for this scenario. Specifically: How should we design the metadata-driven pipeline to dynamically execute 600+ Fabric notebooks? How should we control parallelism when around 40 notebooks need to run simultaneously? Should we use a single Spark environment/pool, multiple environments/pools, or some other approach? How should we decide which Spark compute configuration should be used for each notebook based on: Data volume Memory requirement Processing time Shuffle-intensive workloads Small vs. large workloads? If 40 notebooks run simultaneously, how does Fabric manage the underlying Spark compute and capacity? Should we be concerned about resource contention or throttling? Is it recommended to classify notebooks into workload groups such as:and then control concurrency separately for each group? Small / Medium / Large / XLarge What is the recommended way to implement dependency management? For example:where Notebook B should only start after A succeeds. Notebook A → Notebook B → Notebook C What is the recommended approach for retry, failure handling, logging, and restartability for 600+ notebooks? Is there a recommended metadata-driven orchestration pattern/reference architecture in Microsoft Fabric for this scale? Are there any Fabric-specific limitations or best practices we should consider when running hundreds of Spark notebooks with high concurrency? Our main objective is to build a scalable framework where we can manage 600+ notebooks from metadata instead of maintaining hundreds of individual pipelines, while still controlling Spark compute and parallel execution efficiently. Any architectural recommendations, reference implementations, or real-world experience with similar workloads would be highly appreciated. Thanks!
Fabric Link Setup
Hello Community, I'm trying to Configure Fabric Link with FnO to sync My FnO data to Fabric. While creating Fabric Link for FnO I'm getting 2 Options as per below SS. One is F&O Entities (Caption 1) Second is F&O tables (Caption 2). I want to Understand How "FnO entities" and "FnO tables" different from each other, In which case I need to choose from second Option(F&O tables Caption 2) & in which case I need to choose from (F&O Entities Caption 1). Thank youFabric - VS Code Extension - Conflict Handling
Hi, I've been testing out the extension and whenever I there is a conflict, I'll be prompt to either pick the Local version, Virtual Workspace version or the Compare and Merge. It has been quite annoying when I pick Compare and Merge as I'm trying to compare and decide which one I would like to proceed. But apparently when you click on Compare and Merge, it allows you to compare but you're required to merge. I'm unable to cancel the merge if I try to. Closing the tab doesn't solve the issue. It will only say in Conflict mode for the notebook selected. Is there any explanation or fix for this issue? Thanks
How are AI agents changing modern data engineering workflows?
I am exploring how AI agents can assist data engineering teams in managing complex data workflows and reducing repetitive operational tasks. Some areas I am interested in: AI agents for monitoring data pipelines and detecting failures Automated data quality checks and anomaly detection Intelligent assistance for ETL/ELT workflow optimization Generating documentation for datasets and transformations Using AI with Fabric pipelines, notebooks, and data workflows With the growth of AI-powered automation, I would like to understand how data engineers are approaching these patterns in real-world environments. What approaches, architectures, or best practices are teams using to combine Microsoft Fabric capabilities with AI agents?
Cross-tenant Fabric migration (no shared account): 6 things the docs don't tell you
Sharing this rather than asking — posting my notes in case they save someone else the time. I recently had to move semantic models, reports, notebooks, pipelines and a Lakehouse between two Fabric tenants where no single account had access to both — no guest access, no cross-tenant permissions, two entirely separate sign-ins. Deployment pipelines are same-tenant only. fabric-cicd assumes one tenant and a Git repo. So this became an export-to-file / import-from-file exercise against the REST API. Six things cost me real time. Posting them in case they save someone else the same days. A semantic model's connection lives in FOUR places in model.bim I started by rewriting the M expressions and the log said "0 expressions repointed" while the model kept pointing at the old source. The connection can sit in the M code (Sql.Database(...)), in a dataSources[] entry's connectionDetails.address, in Direct Lake entity partitions, and in M parameters with a meta annotation. Rewriting only one of them silently does nothing. I ended up operating on the raw model.bim JSON text so all four are covered. PBIR report binding changed shape between schema versions definition.pbir validates differently depending on its major version. Schema v2.0+ wants connectionString and nothing else — adding pbiServiceModelId gets you "the schema does not allow additional properties". Schema 1.x wants the full legacy set: pbiServiceModelId, pbiModelVirtualServerName ("sobe_wowvirtualserver"), pbiModelDatabaseName, name: "EntityDataSource", connectionType: "pbiServiceXmlaStyleLive". Omit them and you get "Cannot resolve neither report.json nor the PBIR report content in enhanced format". Preserve the original $schema and version from the source file and branch on the major version. Don't assume one shape. The Lakehouse SQL analytics endpoint is read-only over Delta CREATE VIEW, CREATE FUNCTION and CREATE PROCEDURE all work. CREATE TABLE does not — tables only come from Spark. If you're recreating a Lakehouse structure in a target tenant, that's two separate artefacts: a .sql script for the views and a PySpark notebook for the empty table structure. They also have to run in that order. Notebook writes need ?format=ipynb Creating or updating a notebook definition without it returns "PyToIpynbFailure: Convert py to ipynb failed" with no further explanation. Add the query parameter on both createItem and updateDefinition. Only notebooks need it. Delta rejects column names with spaces or punctuation AnalysisException [DELTA_INVALID_CHARACTERS_IN_COLUMN_NAMES] for anything containing space , ; { } ( ) newline tab = The obvious fix is to sanitise the names, but don't — your views and semantic models reference the original names. Enable column mapping on those tables instead: delta.columnMapping.mode = name, minReaderVersion = 2, minWriterVersion = 5. Original names preserved, Delta happy. Premium_ASWL_Error on refresh does not mean you need a gateway Most threads will tell you to set up an on-premises data gateway. In my case the model was set to "Default: Single Sign-On" and simply needed an explicit cloud connection created and bound to it. No gateway involved. Worth checking before you go down the gateway route. Bonus: pipelines fail with a bare "UnknownError" when they reference items that aren't in the target yet. The error never says which ones. I ended up scanning pipeline definitions for GUIDs and matching them against the source inventory to list the missing items by name before attempting the deploy, and deploying master pipelines after their children. Happy to go deeper on any of these if it's useful.Cross-tenant Fabric migration (no shared account): 6 things the docs don't tell you
Sharing this rather than asking — posting my notes in case they save someone else the time. I recently had to move semantic models, reports, notebooks, pipelines and a Lakehouse between two Fabric tenants where no single account had access to both — no guest access, no cross-tenant permissions, two entirely separate sign-ins. Deployment pipelines are same-tenant only. fabric-cicd assumes one tenant and a Git repo. So this became an export-to-file / import-from-file exercise against the REST API. Six things cost me real time. Posting them in case they save someone else the same days. 1) A semantic model's connection lives in FOUR places in model.bim I started by rewriting the M expressions and the log said "0 expressions repointed" while the model kept pointing at the old source. The connection can sit in the M code (Sql.Database(...)), in a dataSources[] entry's connectionDetails.address, in Direct Lake entity partitions, and in M parameters with a meta annotation. Rewriting only one of them silently does nothing. I ended up operating on the raw model.bim JSON text so all four are covered. 2) PBIR report binding changed shape between schema versions definition.pbir validates differently depending on its major version. Schema v2.0+ wants connectionString and nothing else — adding pbiServiceModelId gets you "the schema does not allow additional properties". Schema 1.x wants the full legacy set: pbiServiceModelId, pbiModelVirtualServerName ("sobe_wowvirtualserver"), pbiModelDatabaseName, name: "EntityDataSource", connectionType: "pbiServiceXmlaStyleLive". Omit them and you get "Cannot resolve neither report.json nor the PBIR report content in enhanced format". Preserve the original $schema and version from the source file and branch on the major version. Don't assume one shape. 3) The Lakehouse SQL analytics endpoint is read-only over Delta CREATE VIEW, CREATE FUNCTION and CREATE PROCEDURE all work. CREATE TABLE does not — tables only come from Spark. If you're recreating a Lakehouse structure in a target tenant, that's two separate artefacts: a .sql script for the views and a PySpark notebook for the empty table structure. They also have to run in that order. 4) Notebook writes need ?format=ipynb Creating or updating a notebook definition without it returns "PyToIpynbFailure: Convert py to ipynb failed" with no further explanation. Add the query parameter on both createItem and updateDefinition. Only notebooks need it. 5) Delta rejects column names with spaces or punctuation AnalysisException [DELTA_INVALID_CHARACTERS_IN_COLUMN_NAMES] for anything containing space , ; { } ( ) newline tab = The obvious fix is to sanitise the names, but don't — your views and semantic models reference the original names. Enable column mapping on those tables instead: delta.columnMapping.mode = name, minReaderVersion = 2, minWriterVersion = 5. Original names preserved, Delta happy. 6) Premium_ASWL_Error on refresh does not mean you need a gateway Most threads will tell you to set up an on-premises data gateway. In my case the model was set to "Default: Single Sign-On" and simply needed an explicit cloud connection created and bound to it. No gateway involved. Worth checking before you go down the gateway route. Bonus: pipelines fail with a bare "UnknownError" when they reference items that aren't in the target yet. The error never says which ones. I ended up scanning pipeline definitions for GUIDs and matching them against the source inventory to list the missing items by name before attempting the deploy, and deploying master pipelines after their children. Happy to go deeper on any of these if it's useful.
