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
Luis González contributed to the pcamarillor/O2025_ESI3914O repository by developing a suite of data engineering features focused on analytics, documentation, and reusable components. He created a Jupyter Notebook for song play analytics, enabling analysis of unique plays and popular songs using Python and SQL. Luis also built a Python-based bank account library with a demonstration notebook, supporting deposit and withdrawal operations. Additionally, he implemented Spark utilities for schema generation and a data pipeline for car rentals, outputting results in Parquet and CSV formats. His work emphasized reproducibility, hands-on learning, and scalable data workflows, demonstrating depth in data engineering practices.
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 people
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
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