Currently, Microsoft Fabric offers three certifications: PL-300 (Power BI Data Analyst) DP-600 (Fabric Analytics Engineer) DP-700 (Fabric Data Engineer Fabric is also becoming a powerful platform for Data Science workflows, especially with capabilities around Lakehouses, Notebooks, Spark, ML integration, and advanced AI tools.   I would like to suggest adding a dedicated Fabric Data Science certification. This certification could cover areas such as: Building and managing data science environments in Fabric Using Lakehouse and Spark for advanced analytics Integrating Machine Learning models with Fabric pipelines Applying responsible AI practices within Fabric End-to-end workflows from data preparation to model deployment Such a certification would provide recognition for data scientists using Fabric and help organizations validate expertise in this growing area. Looking forward to the community’s thoughts on this.  Rufyda Rahma | LinkedIn ... View more

We want the ability for a creator to customize the data agent suggested prompts so that users can select from the applicable prompt suggestions. ... View more

Could we include chart / visualization rendering capability within Fabric data agent. This will be super helpful to visualize the output results while doing Q&A on the data.  ... View more

For deep learning tasks it would be great to have an easy way to use cuda devices in MS Fabric notebooks. ... View more

Summary: Please add support for running Marimo notebooks in Microsoft Fabric. Marimo is a new reactive Python notebook framework that enables fully reproducible, Git-friendly, and app-like data workflows.   Why it matters: Fabric notebooks are great for Spark workloads but limited for lightweight, interactive data apps. Marimo would let users:   Build reactive notebooks that update dynamically Run small workloads without a Spark cluster Version control notebooks as plain .py files Deploy notebooks as interactive web apps (WASM or server mode)     Requested capability:   Allow installing and running the marimo package in Fabric notebooks or Lakehouses (Future) Add a “Marimo Notebook” item type or integration similar to existing notebooks     Impact: Adds a modern, flexible notebook experience in Fabric — bridging traditional notebooks and lightweight web apps, and encouraging open-source integration.   References:   https://docs.marimo.io ... View more

Currently the amount of data that shows up on Fabric data agent after a prompt is very limited. It currently outputs a maximum of 25-30 rows of data. It would be super helpful to output 1000+ rows of data as summarized tables and data can easily reach 1000 rows sometimes across various dimensions. This will significantly help advance the use case of Fabric data agents.  ... View more

Integrate a chat UI for AI skills into organizational apps and make it available to users. Developers can provide a query interface to users without needing to set up Copilot Studio or AI Foundry ... View more

Databricks recently announced the “Assistant Edit Mode,” which allows GitHub Copilot–style editing of entire notebooks. Reference: Databricks Assistant Edit Mode If a similar capability were added to Fabric Notebook Copilot, users could work entirely within Fabric without switching to external editors like VS Code, significantly improving productivity. Suggested capabilities: Use the full notebook context to apply AI-generated suggestions across multiple cells with a single prompt Allow inline or side panel review/approval of changes without regenerating entire cells Support refactoring, variable/function renaming, logic cleanup, and modularization This would greatly enhance code quality and development speed directly within Fabric Notebooks. ... View more

Azure ML Designer has been an excellent tool that allows business users to intuitively build machine learning pipelines. Currently, many business-user-oriented capabilities in Azure ML are no longer being updated, while Fabric is making significant advancements in this area. Proposal: Similar to Fabric Notebooks and Data Pipelines, enable drag-and-drop selection of data from OneLake, followed by data splitting and ML model creation through algorithm selection in a GUI mlflow integration so that execution results (metrics, logs, models) from ML pipelines created in Fabric can be sent to external mlflow servers (e.g., Azure ML) Copilot integration to automatically generate machine learning pipelines from natural language instructions This would allow business users to seamlessly build machine learning workflows entirely within Fabric, significantly reducing the time from data to business value. ... View more

As a Python developer I need to run unit tests on all my PySpark notebook code. All the functionality already exists to run these tests automatically but the naming convention of Notebook files completely ignore python module and package naming rules. There are two issue that would be fantastic to address: The Notebook folder created from git integration should not have periods in the name, eg "my_notebook.Notebook". The notebook python script created from git integration should not have dashes in the name, eg "notebook-content.py" My idea is to simply replace these symbols with underscores. I hope it's a small enough change and it would have a massive impact on enabling automated testing for all Notebook scripts ... View more

The new Rich dataframes in Fabric notebooks are nice but the Chart View still leaves much to be desired for compared other notebook products such as Databricks, HEX and notable. Here are a few proposals and ideas: Similar to Cell and Markdown, create a "Chart" cell type which lets the user pick a dataframe and display it. Users do not need to wrap display(..) every single time. Visuals available in display are very limited and do not allow any formatting or configurations. e.g. you cant change the axis colors, change axis scale. While histogram is available, density plots, scatterplots etc for any serious and meaningful data exploration once created the visuals created w/ display do not persist. e.g. if the notebook author creates a visual, saves the notebook and shares it with someone else, the other users don't see the original chart Fabric already has Quickvisualize and powerbiclient which allow use of Power BI in notebooks but a deeper, native integration to leverage Power BI inside Fabric notebooks would be great especially for those who are new to notebooks as well data scientists who can use UI option in the notebooks for visualization. ... View more

most of our data processing code was implemented in Scala. This choice was driven by Scala’s native integration with Spark, superior performance, full access to Spark features, strong compile-time safety, and more mature tooling and ecosystem support at the time. While PySpark has significantly improved—offering near API parity, better performance, and support for the Pandas API—migrating all our existing Scala code to Python might not provide the best user experience and could introduce unnecessary complexity.   Could you provide Scala support in AI features? ... View more

When training a Data Agent I think it would be good if you included a checkbox where a user could indicate the SQL or DAX query generated correctly answers the prompt. ... View more

The great thing about Fabric is that we can share data between Lakehouse and Warehouse in one workspace. However, the utility is massively unlocked when we can share workload/processing/pipeline across the workspace too. This means the ability to run a bit of Spark notebook, then a stored proc, then another notebook, then stored proc again etc. Usually a mixed pipeline would need a secondary tool like Airflow or Prefect. However, if we can execute T-SQL stored procs from the Notebook, then we can orchestrate a pipeline across lakehouse/warehouse from within Fabric itself. This will be massive. Please add the ability to easily execute Warehouse T-SQL from Spark notebook. Presumably we can try to do this by setting up the Apache Spark connector but it would be quite complicated. ... View more

Currently we can run Open AI services for in Azure Synapse Analytics in lowest possible spark compute, which is most cost effective for us to have a dev playground. Spinning up an F64 just so that people can test it is not the best solution available. ... View more

In RStudio, VSCode and Positron, when you write the name of a dataframe followed by $, an autocomplete dropdown list appears with every column name in that dataframe. Please add this feature to Fabric Notebooks. It is essential for anyone that uses SparkR, especially if you have tables with many columns.     ... View more

It would be helpful to the significant group of R Users if Fabric had support for "R Environments" in the same way that Python is supported. With Python environments, I can specify, download, and use libraries (including the specific versions) that I would like to use in my sessions. This is facilitated via the "Environment" object in the Fabric workspace. This functionality does not exist for R. While it is possible to download and use libraries (including the specific versions) in the CRAN repository, I need to re-download the libraries whenever I start a new session and this can take some time, slowing down my analysis, especially if my session is prone to crashing. Essentially, it would be great if there was a functionality to create R environments without needing to do session-level installs. Is this on the radar? If so, how soon until this is implemented? Thanks! ... View more