Forum Discussion

data_quantum's avatar
data_quantum
New Member
2 years ago

Sharing data progress outside Fabric Notebook

Hi,    I am connecting to a Lakehouse from a Notebook within Microsoft Fabric. I run an analysis on a dataframe, e.g. ydata_profiler using a PySpark, that produces an inline report of my data. Grea...
Data Science
anilgavhane's avatar
anilgavhane
Super User
10 months ago

What Doesn’t Work Well

  • PDFs: Static, not suitable for interactive profiling reports.
  • Power BI: Powerful but often overkill for quick sharing, and adds friction.
  • Streamlit inside Fabric: Not currently supported natively within Fabric notebooks due to environment limitations.

 

Best Ways to Share Interactive Reports Outside Fabric

1. Export Data + Host Streamlit Locally

Since Streamlit doesn’t run inside Fabric, you can:

  • Export your processed data (e.g. as CSV or Parquet) from the notebook.
  • Build a local Streamlit app on your laptop using ydata-profiling or pandas-profiling.
  • Share the app via:
  • Internal web server (e.g. localhost or intranet)
  • Streamlit Cloud (for public or semi-private sharing)
  • Docker container (for reproducibility)

This gives you full interactivity and control.

2. Use HTML Export from Profiling Tools

If you're using ydata-profiling, you can export the report as an HTML file:

 

profile = ProfileReport(df) profile.to_file("report.html")

 

Then share the HTML file via email, Teams, or a shared drive. It’s interactive and lightweight—no need for Streamlit.

3. Fabric + OneLake + External App

  • Store the processed data in OneLake.
  • Build a lightweight web app (Streamlit, Dash, Flask) outside Fabric that reads from OneLake.
  • This separates compute from presentation and avoids tying the report to your notebook session.

  • binitafulpagare's avatar
    binitafulpagare
    Kudo Collector
    27 days ago

    Hi anilgavhane,

    Thank you for sharing such a comprehensive overview of the available options.

    I particularly appreciate the comparison of different approaches and the explanation of their trade-offs. Using HTML export from ydata-profiling is a simple and effective way to share interactive reports, while the approach of combining OneLake with an external application such as Streamlit or Dash provides a scalable architecture for organizations that need richer stakeholder experiences.

    Your recommendation to separate the analytics layer from the presentation layer is especially valuable, as it improves flexibility, maintainability, and collaboration without tying business users to the notebook environment.

    Thank you again for taking the time to share these practical solutions. Your guidance will be helpful for anyone looking to share interactive data science outputs more effectively outside Microsoft Fabric.