pcamarillor/O2025_ESI3914O
Sep 2025 – Sep 2025
1 Month active
Languages Used
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Big Data ProcessingData AnalysisData EngineeringData PipelinesData ProcessingData Structures
LinguiniPanini
PROFILE
Linguinipanini
Contributed to the pcamarillor/O2025_ESI3914O repository by developing four new features focused on data engineering and educational tooling. Built a reusable Python library for bank account management, complete with a demonstration notebook, and enhanced analytics capabilities through a Jupyter Notebook analyzing song play data. Improved Spark-based data processing pipelines by adding schema generation utilities and a car rentals workflow with Parquet and CSV outputs. Refactored existing labs for clarity and maintainability, and expanded documentation to support collaborator onboarding. Leveraged Python, PySpark, and SQL to deliver reproducible, scalable solutions that streamline data workflows and support hands-on learning in the repository.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
1,720
Activity Months1
Your Network
15 peopleShared Repositories
15
Arantxa AnguloMember
JosMember
Renata TejedaMember
EduNav04Member
thexavirooMember
perrionskyMember
Juan LópezMember
luci-efeMember
danielarellMember
Work History
September 2025
7 Commits • 4 Features
Sep 1, 2025In September 2025, delivered a cohesive set of data engineering and learning-material improvements for pcamarillor/O2025_ESI3914O, focusing on documentation, analytics, reusable libraries, and Spark-based data processing. These contributions enhance reproducibility, hands-on learning, and scalable data workflows while delivering tangible business value through clearer documentation, actionable analytics capabilities, and reusable components.
7 Commits • 4 Features
Sep 1, 2025In September 2025, delivered a cohesive set of data engineering and learning-material improvements for pcamarillor/O2025_ESI3914O, focusing on documentation, analytics, reusable libraries, and Spark-based data processing. These contributions enhance reproducibility, hands-on learning, and scalable data workflows while delivering tangible business value through clearer documentation, actionable analytics capabilities, and reusable components.
September 2025
Activity
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Quality Metrics
Correctness88.6%
Maintainability85.8%
Architecture82.8%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Big Data ProcessingData AnalysisData EngineeringData PipelinesData ProcessingData StructuresDocumentationETLJupyter NotebooksObject-Oriented ProgrammingPySparkPythonPython DevelopmentPython ProgrammingSQL
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline