Data Science topics

FABRIC REST API Connections - Create Connection

Charline_74 — Wed, 16 Sep 2026 14:29:45 GMT

Does anyone know how to run this API via a PowerShell script to create a SQL Server cloud connection for a semantic model using Service Principal authentication?

Here's what I have so far:

Invoke-PowerBIRestMethod -Url "https://api.fabric.microsoft.com/v1/connections" -Method Post -Body $body

But I don't understand how to construct the body

Thanks in Advance,

Charline

Certificate validation error between Fabric and Huggingface

pborah — Wed, 26 Aug 2026 17:25:07 GMT

Hi,

I'm tyring to download a couple of transformer/sentence-transformer model weights into a notebook and I keep running into certificate mismatch issues between fabric and huggingface. I was wondering if anyone else has ran into this issue or if this is a known issue that Microsoft plans on handling in the very near future?

Environment

Microsoft Fabric Notebook (PySpark)

Python 3.11

PyTorch 2.2.1

Canada tenant

sentence-transformers==2.7.0 successfully installed

What works

Package installation succeeds

%pip install sentence-transformers==2.7.0

Import succeeds

from sentence_transformers import SentenceTransformer

version check succeeds

import torch import sentence_transformers print(torch.__version__) print(sentence_transformers.__version__)

What fails

from sentence_transformers import SentenceTransformer model = SentenceTransformer("all-MiniLM-L6-v2")

Observations

The notebook appears able to reach Hugging Face and download metadata files. For example, the following files are successfully cached:

config.json config_sentence_transformers.json modules.json README.md sentence_bert_config.json

However, the model weight file never downloads. The Hugging Face cache contains *.incomplete with a file size of 0 MB.

Error

The relevant error message appears to be

"No CA certificates were loaded from the system"

Is downloading Hugging Face model weights from Fabric notebooks currently supported? If so, is there a known issue with certificate validation? Is there a recommended workaround?

If not. then is there a recommended approach for generating semantic text embeddings within Fabric notebooks utilizing some kind of ANN based algorithm instead of classic techniques such as TF-IDF or SVD followed by some type of clustering?

The goal of this notebook is semantic clustering of approximately 500 short "type" descriptions for taxonomy development.

Thanks!

Data science roadmap

powerbidev123 — Sun, 02 Aug 2026 13:37:35 GMT

OneLake is often highlighted as one of the key capabilities of Microsoft Fabric, especially for centralizing and managing enterprise data. I'm interested in hearing from those who are using it in production environments.

For those with hands-on experience:

What challenges have you encountered while implementing or managing OneLake?
Have you experienced any performance or scalability issues as your data volumes increased?
How do you structure and organize data across workspaces, domains, or business units in a large enterprise?
What governance, security, and access control practices have worked well for your organization?
Are there any lessons learned or best practices you wish you had known before adopting OneLake?

I'd love to hear about real-world experiences, whether positive or challenging. Your insights could be incredibly valuable for others planning or expanding their Microsoft Fabric adoption.

PBI to data science

powerbidev123 — Sun, 02 Aug 2026 13:29:18 GMT

A sales forecasting model is built in Python. How would you present the results in Power BI?
A fraud detection model produces a fraud probability score. How would you design a dashboard for business users?
Customer churn predictions are generated daily. How would you create an executive dashboard?
How would you visualize SHAP values or feature importance in Power BI?
A model's accuracy drops from 92% to 80%. How would you build a dashboard to help investigate the issue?

Fabric Data Agent published to M365 has lost conversational context since yesterday

John_DataYoda — Tue, 28 Jul 2026 11:31:20 GMT

Hello,

I’m experiencing a strange issue with a Fabric Data Agent that has been published to Microsoft 365.

The agent continues to work correctly when accessed directly from Fabric. However, when using the same agent through Microsoft 365, it appears to have lost its ability to maintain conversational context and follow-up questions.

Up until Friday, everything was working as expected, and I have not made any changes to the agent configuration, semantic model, capacity, permissions, or data sources.

Example:

User: Give me the payments for July.

Agent: (Returns the correct results.)

User: Give me the payments for June as well.

Agent: "What would you like me to provide?"

Previously, the agent correctly understood that the follow-up question referred to payments and would return the requested information. Now it seems unable to retain context between messages.

The issue only occurs in the Microsoft 365 published version of the agent. The same agent continues to behave correctly inside Fabric.

Has anyone else noticed a similar degradation in conversational context retention or follow-up question handling during the last couple of days?

Any feedback would be appreciated.

Thank you.

Data Science tutorial on github with the Bank Churn Dataset is using a link that responds with 404

curiouskay — Tue, 28 Jul 2026 10:24:28 GMT

Hello everyone,

It's just as the subject says. I tried to ingest the dataset into my lakehouse to work on it.

The code ran and printed 'the files have been downloaded'. I kept refreshing and didn't see any files, so I checked the link using the requests library if the link responds with anything, I got <Response [404]>.

Just letting people in the community know to save them some time. Hope this is resolved soon as it is in the official Microsoft Documentation here.

Getting Started with Machine Learning in Microsoft Fabric

tanziyaashaq — Sat, 25 Jul 2026 13:06:59 GMT

Getting Started with Machine Learning in Microsoft Fabric

Microsoft Fabric brings data engineering, data science, and analytics together in one unified platform, making it easier to build and manage machine learning solutions.

With Fabric, you can: ✅ Prepare and transform data efficiently

✅ Build and train ML models using notebooks

✅ Track experiments and compare model performance

✅ Deploy models for real-world predictions

✅ Collaborate with your team in a single workspace

Machine learning isn't just about creating accurate models—it's about turning data into actionable insights that drive better decisi

Starting Data Science journey with Python - What is the next step after lists?

Surhan — Sat, 25 Jul 2026 08:39:43 GMT

Hello everyone!

I am a beginner starting my journey in Data Science. I have recently learned the basics of Python, including lists.

Could anyone guide me on what I should learn next in Python for Data Science? Also, as a beginner, what is the best way to practice using Microsoft Fabric for Data Science?

Thank you!

Data science

pinkuk — Thu, 23 Jul 2026 16:46:48 GMT

Hello, I am a student from India interested in data science and cloud. I am new to Microsoft Fabric and want guidance on how to start learning data science with Fabric. Please suggest beginner‑friendly videos, documentation, and small project ideas. Thank you.

What the difference between lake house and warehouse in fabric?

Areeshabaloch7 — Tue, 21 Jul 2026 16:22:23 GMT

Hello everyone,

I am new to Microsoft Fabric.

Can someone please explain in simple words the difference between Lakehouse and Warehouse?

When should we use Lakehouse and when should we use Warehouse?

Thank you!

Best Practices for Organizing Lakehouses in Microsoft Fabric

binitafulpagare — Sun, 19 Jul 2026 14:33:54 GMT

As organizations adopt Microsoft Fabric across multiple business units, I've noticed that designing an effective Lakehouse structure becomes increasingly important for scalability, governance, and collaboration.

Some organizations appear to create a separate Lakehouse for each business domain, while others prefer a centralized approach with shared datasets and controlled access.

I'm interested in understanding the approaches that have worked well in production environments.

Some areas I'd like to learn more about are:

How do you decide when to create a new Lakehouse versus extending an existing one?
What folder and table organization strategies have proven effective as data volumes grow?
How do you balance data sharing with security and governance across different teams?
Are OneLake Shortcuts commonly used to reduce duplication between Lakehouses?
What naming conventions or architectural standards have helped simplify long-term maintenance?

I'd appreciate hearing about real-world experiences, lessons learned, and best practices from organizations running Microsoft Fabric at enterprise scale.

ML Deployment

binitafulpagare — Fri, 17 Jul 2026 04:04:28 GMT

Hi everyone,

I'm learning how organizations move machine learning models from experimentation to production using Microsoft Fabric.

For those working in production environments:

How do you deploy models?
How do you monitor model performance?
How do you manage retraining?
Which Fabric services do you use throughout the ML lifecycle?

I'd appreciate hearing about your architecture and best practices.

Thank you!

Hidden Features

binitafulpagare — Thu, 16 Jul 2026 06:39:14 GMT

Hi everyone,

Sometimes the most valuable features of a platform are the ones that don't receive much attention.

In Microsoft Fabric, are there any features, capabilities, or workflows that you believe are underrated but have significantly improved your productivity?

It could be something related to notebooks, pipelines, shortcuts, monitoring, governance, Power BI integration, or any other part of Fabric.

I'd love to discover features that experienced users rely on but aren't discussed very often.

Thanks in advance for sharing your recommendations.

Production Lessons

binitafulpagare — Thu, 16 Jul 2026 06:38:40 GMT

Hi everyone,

Documentation often explains how features work, but production environments usually reveal unexpected challenges.

For those who have deployed Microsoft Fabric in production:

What was the biggest lesson you learned after go-live that you wish you had known earlier?

Whether it relates to architecture, governance, performance, security, collaboration, or cost optimization, I'd really appreciate hearing your experience.

Your insights could help many learners avoid common mistakes.

Thank you!

Real-Time Analytics

binitafulpagare — Thu, 16 Jul 2026 06:38:07 GMT

Hi everyone,

Microsoft Fabric provides several capabilities for real-time analytics, but I'm curious about how organizations are actually using them.

If you've implemented real-time solutions:

Which business scenarios benefited the most?
Which Fabric components do you use most frequently?
What challenges did you encounter while processing streaming data?
How do you balance latency, scalability, and cost?

I'd love to hear examples from real-world implementations.

Thanks for sharing your experience!

Data Quality

binitafulpagare — Thu, 16 Jul 2026 06:37:30 GMT

Hi everyone,

One challenge I keep thinking about is maintaining high data quality across enterprise data platforms.

For those working with Microsoft Fabric:

How do you validate incoming data?
Do you automate quality checks within pipelines?
Which tools or techniques have proven most effective?
How do you handle unexpected schema changes or invalid records?

I'm interested in learning about practical strategies that have worked well in production environments.

Thank you!

Git Workflow

binitafulpagare — Thu, 16 Jul 2026 06:36:58 GMT

Hi everyone,

Version control is essential for collaborative analytics projects.

For teams using Git with Microsoft Fabric:

How do you organize repositories?
How do you manage branching strategies?
Have you integrated Fabric with CI/CD pipelines?
What challenges did you face when multiple developers worked on the same project?

I'd appreciate hearing how your development workflow has evolved over time.

Looking forward to your recommendations.

Semantic Models

binitafulpagare — Thu, 16 Jul 2026 06:36:14 GMT

Hello everyone,

As Microsoft Fabric projects become larger, managing semantic models efficiently becomes increasingly important.

I'd like to know how experienced teams approach semantic model design.

Do you create one centralized model for the organization, or multiple domain-specific models? How do you balance performance, maintainability, and business requirements?

Any design recommendations or lessons learned would be greatly appreciated.

Thank you!

Direct Lake

binitafulpagare — Thu, 16 Jul 2026 06:35:39 GMT

Hi everyone,

Direct Lake is often described as one of Microsoft's most significant innovations for Power BI and Fabric.

For those using Direct Lake in production:

How has it performed compared to Import mode or DirectQuery?
Have you encountered any limitations or scenarios where Direct Lake wasn't the best choice?
What best practices would you recommend before implementing it?

I'd love to hear about your real-world experiences rather than benchmark results.

Thanks!

Fabric Capacity

binitafulpagare — Thu, 16 Jul 2026 06:34:54 GMT

Hi everyone,

Capacity planning seems to be one of the most important aspects of successfully running Microsoft Fabric in production.

For those managing enterprise environments:

How do you estimate the right Fabric capacity before deployment?
Have you ever underestimated or overestimated your capacity needs?
Which workloads consume the most resources in your environment?
What monitoring practices help you avoid performance issues?

I'd appreciate any practical advice or lessons learned.

Thank you!