pcamarillor/O2025_ESI3914B
Aug 2025 – Oct 2025
3 Months active
Languages Used
MarkdownJSONJupyter NotebookPythonSQL
Technical Skills
DocumentationPersonal BrandingApache SparkBig DataData AnalysisData Engineering
Juan Alonso
PROFILE
Juan Alonso
Juan Alonso contributed to the pcamarillor/O2025_ESI3914B repository by developing data engineering features and improving onboarding processes over three months. He built a PySpark and Neo4j pipeline for ingesting UK train station data, enabling CSV-to-graph transformations and verification queries. Juan enhanced lab reproducibility by creating a bank account management module in Python, designing playlist analytics in Jupyter Notebooks, and standardizing lab templates. He also implemented Spark Structured Streaming for real-time log processing and improved documentation with contributor profiles. His work demonstrated depth in big data processing, module development, and cross-technology integration, resulting in more maintainable and scalable educational workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
8
Lines of code
3,032
Activity Months3
Your Network
40 people
Work History
October 2025
3 Commits • 3 Features
Oct 1, 2025Month: 2025-10 — Delivered end-to-end data ingestion, visualization, and streaming capabilities in pcamarillor/O2025_ESI3914B. Implemented UK Train Station Data Ingestion Pipeline (PySpark + Neo4j) for CSV-to-graph ingestion with PySpark verification queries. Enhanced notebook visuals with an embedded image in Markdown. Launched Structured Streaming for log processing (directory-based ingestion, parsing, critical-error filtering, console output). Major bugs fixed: none reported; existing components stabilized. Technologies demonstrated: PySpark, Neo4j, Spark Structured Streaming, CSV parsing, graph data modeling, and notebook data storytelling.
3 Commits • 3 Features
Oct 1, 2025Month: 2025-10 — Delivered end-to-end data ingestion, visualization, and streaming capabilities in pcamarillor/O2025_ESI3914B. Implemented UK Train Station Data Ingestion Pipeline (PySpark + Neo4j) for CSV-to-graph ingestion with PySpark verification queries. Enhanced notebook visuals with an embedded image in Markdown. Launched Structured Streaming for log processing (directory-based ingestion, parsing, critical-error filtering, console output). Major bugs fixed: none reported; existing components stabilized. Technologies demonstrated: PySpark, Neo4j, Spark Structured Streaming, CSV parsing, graph data modeling, and notebook data storytelling.
October 2025
September 2025
10 Commits • 4 Features
Sep 1, 2025September 2025: Delivered lab enhancements across pcamarillor/O2025_ESI3914B, increasing reproducibility and instructional value. Key features delivered include the Bank Account Management Module with a new bank_class.py and notebook integration (deposits, withdrawals, balance checks) and notebook refresh via importlib; Lab 01 Playlist Analysis Notebook with end-to-end playlist analytics (duplicate removal, per-user unique song counts, popular songs) and sample data; Notebook Cleanup and Lab Templates to standardize language sections and support submission workflows; Spark Lab enhancements including Celsius-Fahrenheit MapReduce exercise, SparkUtils utilities, and improved schema generation for Spark SQL workflows. No major bugs reported this month; minor cleanup tasks completed. Overall impact: more scalable labs, faster onboarding, and richer data-processing demonstrations for students. Technologies demonstrated: Python module development, importlib-based notebook refresh, enhanced notebook workflows, and Spark (Spark SQL, MapReduce, schema utilities).
September 2025
10 Commits • 4 Features
Sep 1, 2025September 2025: Delivered lab enhancements across pcamarillor/O2025_ESI3914B, increasing reproducibility and instructional value. Key features delivered include the Bank Account Management Module with a new bank_class.py and notebook integration (deposits, withdrawals, balance checks) and notebook refresh via importlib; Lab 01 Playlist Analysis Notebook with end-to-end playlist analytics (duplicate removal, per-user unique song counts, popular songs) and sample data; Notebook Cleanup and Lab Templates to standardize language sections and support submission workflows; Spark Lab enhancements including Celsius-Fahrenheit MapReduce exercise, SparkUtils utilities, and improved schema generation for Spark SQL workflows. No major bugs reported this month; minor cleanup tasks completed. Overall impact: more scalable labs, faster onboarding, and richer data-processing demonstrations for students. Technologies demonstrated: Python module development, importlib-based notebook refresh, enhanced notebook workflows, and Spark (Spark SQL, MapReduce, schema utilities).
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 — Focused on improving contributor onboarding and repository collaboration in pcamarillor/O2025_ESI3914B. Delivered contributor profile documentation and established a collaborator profile to streamline future contributions. No major bugs were reported this month. The work enhances transparency, onboarding, and long-term maintainability of the project.
1 Commits • 1 Features
Aug 1, 2025August 2025 — Focused on improving contributor onboarding and repository collaboration in pcamarillor/O2025_ESI3914B. Delivered contributor profile documentation and established a collaborator profile to streamline future contributions. No major bugs were reported this month. The work enhances transparency, onboarding, and long-term maintainability of the project.
August 2025
Activity
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Quality Metrics
Correctness85.8%
Maintainability84.2%
Architecture81.4%
Performance81.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Apache SparkBig DataBig Data ProcessingData AnalysisData EngineeringDocumentationGraph DatabasesImportlibJupyter NotebookJupyter NotebooksMapReduceModule DevelopmentNeo4jObject-Oriented ProgrammingPersonal Branding
Repositories Contributed To
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