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Phillip Stewart

PROFILE

Phillip Stewart

Philip Stewart contributed to the microsoft/fabric-user-data-functions-samples repository by engineering robust data handling and deployment solutions for user-defined functions. He refactored Python UDFs to use native data structures, improving input/output reliability and enabling smoother integration with analytics pipelines. Stewart standardized Lakehouse client naming conventions to reduce errors and enhance code maintainability. He also optimized packaging and deployment processes by ensuring binary assets were included in distribution archives, aligning runtime requirements with deployment workflows. His work leveraged Python, SQL, and data engineering skills to improve template usability, documentation accuracy, and onboarding consistency, demonstrating a thoughtful approach to maintainable and reproducible data solutions.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

8Total
Bugs
0
Commits
8
Features
4
Lines of code
84
Activity Months3

Work History

September 2025

2 Commits • 1 Features

Sep 1, 2025

September 2025 monthly summary focusing on asset/template refresh and release hygiene for the fabric-user-data-functions-samples repository. No code changes were introduced this month; emphasis was on ensuring the latest templates and binary assets align with documentation and examples, improving deployment consistency and developer onboarding.

August 2025

4 Commits • 1 Features

Aug 1, 2025

Month: 2025-08 — Packaging and deployment optimization for HelloFabric UDF templates in microsoft/fabric-user-data-functions-samples. Delivered an enhancement to include the bin directory in both deploy and source zip archives, ensuring runtime assets are present and deployments are reproducible. This aligns packaging with runtime requirements and reduces post-deploy issues.

December 2024

2 Commits • 2 Features

Dec 1, 2024

In December 2024, delivered focused improvements to the microsoft/fabric-user-data-functions-samples repository, emphasizing robust UDF input/output handling and consistent Lakehouse client naming to improve downstream data consumption, reliability, and developer experience.

Activity

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Quality Metrics

Correctness87.6%
Maintainability87.6%
Architecture87.6%
Performance87.6%
AI Usage20.0%

Skills & Technologies

Programming Languages

BinaryPythonSQL

Technical Skills

Data EngineeringData ManipulationDeploymentLakehouseLakehouse InteractionNumpyPackagingPandasPythonSQL Database Interaction

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

microsoft/fabric-user-data-functions-samples

Dec 2024 Sep 2025
3 Months active

Languages Used

PythonSQLBinary

Technical Skills

Data EngineeringData ManipulationLakehouseLakehouse InteractionNumpyPandas

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