Save Time with Custom Python Modules in Microsoft Fabric

While we wait for the video session to come online,I have instead created a LinkedIn article with the key points from the session.

If you’re a data professional leveraging Microsoft Fabric, you’re likely juggling repetitive tasks that can slow down your workflows. Last week, we explored a practical solution to this problem: custom Python modules in Microsoft Fabric. Here's how they can save time, improve collaboration, and boost efficiency.

Why Custom Python Modules?

Repetitive code not only increases the risk of errors but also creates a barrier to scaling your data solutions. Enter the ‘Don’t Repeat Yourself’ (DRY) principle, which advocates for reducing redundancy in your codebase. Custom Python modules embody this principle, offering a way to:

1. Promote Code Reusability: Functions and logic can be packaged into modules and reused across multiple projects, cutting down on development time.
2. Enhance Collaboration: Standardized modules make it easier for teams to work together, reducing inconsistencies and streamlining onboarding.
3. Simplify Maintenance: Updating a single module propagates changes across all projects using it, reducing technical debt.

Getting Started with Custom Modules in Fabric

Step 1: Creating Your Module Bas Land demonstrated how to create a Python module in Visual Studio Code. Start by:

• Creating a Python project folder.
• Adding your reusable functions into .py files.
• Organizing files into a structured hierarchy for clarity.

Step 2: Packaging the Module Turn your project into a distributable package by:

• Creating a setup.py file.
• Running python setup.py bdist_wheel to generate a wheel file.

Step 3: Importing into Fabric Upload the wheel file to your Microsoft Fabric environment, and import it into your notebooks or pipelines. Bas’s live demo showed how seamless this process can be—from creating Spark dataframes to running operations on Delta tables using modular functions.

Real-World Benefits

Here are some takeaways from Bas’s session:

• Scalability: Bas’s ‘upset’ function for Delta tables demonstrates how a single function can handle table creation, updates, and merges efficiently.
• Cost-Effectiveness: By leveraging parallelized notebooks and worker orchestration, repetitive tasks can be performed faster and cheaper.
• Community-Driven Innovation: Python’s robust library ecosystem and community support make it easy to extend your functionality or troubleshoot challenges.

Key Insights from the Session

Bas also addressed:

• Git Integration: Managing version control for Python modules using Azure DevOps or GitHub ensures a reliable development workflow.
• Open-Source Frameworks: Attendees were encouraged to explore Microsoft’s metadata-driven Fabric framework for additional inspiration.

Next Steps

If you’re ready to supercharge your data engineering with Microsoft Fabric, here’s what to do next:

1. Experiment with creating and importing custom Python modules.
2. Consider version control tools like Azure DevOps for managing your projects.
3. Explore pre-built frameworks such as the Microsoft Fabric metadata-driven framework.
4. Stay updated by following https://thatfabricguy.com/author/admin/ for more tips and resources or read the official documentation https://learn.microsoft.com/en-us/fabric/data-engineering/library-management

Upcoming Events

Don’t miss these sessions to deepen your Fabric expertise:

• Jan 21: Fabric Databases vector search and AI.
• Jan 28: Fabric Adoption Strategies.
• Feb 8: Data Saturday at Microsoft HQ.
• Feb 11: Intro to PySpark in Fabric.

Let’s unlock the full potential of Microsoft Fabric together. Join the conversation and connect with thought leaders like Bas Land on LinkedIn for insights and inspiration!