pcamarillor/O2025_ESI3914B
Aug 2025 – Oct 2025
3 Months active
Languages Used
JSONPythonSQLJupyter Notebook
Technical Skills
Data AnalysisData ProcessingJupyter NotebookPython ScriptingBig DataBig Data Processing
Diego Orozco
PROFILE
Diego Orozco
Diego Orozco developed a series of data engineering and analytics features for the pcamarillor/O2025_ESI3914B repository, focusing on end-to-end workflows in Python, PySpark, and Neo4j. He built Jupyter Notebooks for music streaming analytics, banking simulations, and airline data cleaning, implementing class-based designs, schema-driven transformations, and structured streaming pipelines. His work included graph database ingestion for artist and lyrics data, real-time log processing, and robust data export in Parquet and CSV formats. Diego emphasized reproducibility and validation, creating reusable modules and dynamic schema utilities that enabled scalable analytics, reliable data integration, and streamlined reporting across multiple business domains.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
9
Lines of code
4,490
Activity Months3
Your Network
40 people
Work History
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for pcamarillor/O2025_ESI3914B. Delivered two end-to-end data engineering notebooks enabling graph-based analytics and real-time streaming capabilities. Key work includes Lab 06: Neo4j Data Ingestion, Graph Creation, and Verification, and Lab 07: Structured Streaming with Files. Brought forward end-to-end data pipelines: from ingestion to graph-based storage and verification, and from synthetic log generation to real-time streaming processing. No major bugs reported; minor notebook/environment tweaks were performed to improve reproducibility across environments. Impact: Enables reproducible graph analytics on artist/lyrics data and real-time log processing capabilities, accelerating validation and insights, and establishing a reusable blueprint for future labs. Technologies/skills demonstrated: Neo4j, Jupyter Notebooks, Python, Spark (Structured Streaming), SparkSession management, and data generation modules. Commits contributing to this work: - Lab06_diego_orozco (fddff85e8cf35daa4f430d4d8d20e6434fba5f91) - Lab07_diego_orozco (5f2b9ea65fe82496ad590f3afbf6e412b303eae0)
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for pcamarillor/O2025_ESI3914B. Delivered two end-to-end data engineering notebooks enabling graph-based analytics and real-time streaming capabilities. Key work includes Lab 06: Neo4j Data Ingestion, Graph Creation, and Verification, and Lab 07: Structured Streaming with Files. Brought forward end-to-end data pipelines: from ingestion to graph-based storage and verification, and from synthetic log generation to real-time streaming processing. No major bugs reported; minor notebook/environment tweaks were performed to improve reproducibility across environments. Impact: Enables reproducible graph analytics on artist/lyrics data and real-time log processing capabilities, accelerating validation and insights, and establishing a reusable blueprint for future labs. Technologies/skills demonstrated: Neo4j, Jupyter Notebooks, Python, Spark (Structured Streaming), SparkSession management, and data generation modules. Commits contributing to this work: - Lab06_diego_orozco (fddff85e8cf35daa4f430d4d8d20e6434fba5f91) - Lab07_diego_orozco (5f2b9ea65fe82496ad590f3afbf6e412b303eae0)
October 2025
September 2025
8 Commits • 6 Features
Sep 1, 2025September 2025 performance recap for pcamarillor/O2025_ESI3914B focused on delivering robust data processing features, validation, and export-ready workloads that drive lab analytics and scalability. No major bugs reported this period; all changes emphasize business value and reproducible data pipelines.
September 2025
8 Commits • 6 Features
Sep 1, 2025September 2025 performance recap for pcamarillor/O2025_ESI3914B focused on delivering robust data processing features, validation, and export-ready workloads that drive lab analytics and scalability. No major bugs reported this period; all changes emphasize business value and reproducible data pipelines.
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08: Implemented the Music Streaming Analytics Notebook for pcamarillor/O2025_ESI3914B to enable end-to-end processing of streaming data. The notebook handles duplicate play records, computes unique songs listened per user, and derives a popularity metric to identify top tracks. Outputs are emitted in structured JSON formats for both song-level and user-level insights, enabling downstream analytics and reporting. This work establishes a reproducible data-processing workflow and provides a foundation for scalable music analytics and performance reporting.
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08: Implemented the Music Streaming Analytics Notebook for pcamarillor/O2025_ESI3914B to enable end-to-end processing of streaming data. The notebook handles duplicate play records, computes unique songs listened per user, and derives a popularity metric to identify top tracks. Outputs are emitted in structured JSON formats for both song-level and user-level insights, enabling downstream analytics and reporting. This work establishes a reproducible data-processing workflow and provides a foundation for scalable music analytics and performance reporting.
August 2025
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Quality Metrics
Correctness80.0%
Maintainability78.2%
Architecture74.6%
Performance72.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonSQL
Technical Skills
Big DataBig Data ProcessingClass ImplementationData AnalysisData CleaningData EngineeringData ProcessingData WarehousingFile Formats (Parquet, CSV)Graph DatabasesJSON ParsingJupyter NotebookJupyter NotebooksLog AnalysisNeo4j
Repositories Contributed To
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