pcamarillor/O2025_ESI3914B
Sep 2025 – Oct 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonSQLShell
Technical Skills
Apache SparkBig Data ProcessingData AnalysisData CleaningData EngineeringData Processing
Axel
PROFILE
Axel
Over a two-month period, contributed to the pcamarillor/O2025_ESI3914B repository by developing six end-to-end data engineering features focused on education and analytics. Delivered Jupyter lab notebooks and Spark environment setups to streamline student onboarding, built a reusable Spark SQL schema generator in Python, and implemented a consolidated rentals analytics pipeline using PySpark and SQL. Extended the project with a Neo4j graph ingestion workflow and a real-time log analysis system leveraging Structured Streaming. Emphasized reproducibility and onboarding through detailed documentation and lab-driven deliverables, applying skills in Apache Spark, data cleaning, schema definition, and graph database integration without reported production bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
6
Lines of code
2,363
Activity Months2
Your Network
40 peopleWork History
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025: Delivered two end-to-end data engineering features in pcamarillor/O2025_ESI3914B, establishing tangible business value through graph-based relationships and real-time monitoring. Key work includes an end-to-end Neo4j graph ingestion pipeline using PySpark (CSV ingestion, transformation to graph nodes/edges, persistence to Neo4j, and verification via queries) and a Real-time Log Analysis workflow with PySpark Structured Streaming (file-source streaming, a Python log simulator, and a Jupyter notebook for filtering critical errors). No major bugs were reported this month. Commits documenting Lab 6 and Lab 7 underpin reproducibility and knowledge transfer.
2 Commits • 2 Features
Oct 1, 2025October 2025: Delivered two end-to-end data engineering features in pcamarillor/O2025_ESI3914B, establishing tangible business value through graph-based relationships and real-time monitoring. Key work includes an end-to-end Neo4j graph ingestion pipeline using PySpark (CSV ingestion, transformation to graph nodes/edges, persistence to Neo4j, and verification via queries) and a Real-time Log Analysis workflow with PySpark Structured Streaming (file-source streaming, a Python log simulator, and a Jupyter notebook for filtering critical errors). No major bugs were reported this month. Commits documenting Lab 6 and Lab 7 underpin reproducibility and knowledge transfer.
October 2025
September 2025
6 Commits • 4 Features
Sep 1, 2025September 2025 monthly summary for pcamarillor/O2025_ESI3914B: Key features delivered: - Course Lab Notebooks for Autumn 2025 (Lab 02 and Lab 04): user-facing lab notebooks and Spark environment setup to accelerate student onboarding and hands-on practice. - Lab 03 Notebook and Solution (Data Cleaning and Feature Engineering on Flight Data): end-to-end notebook for data cleaning, normalization, null handling, and feature engineering; accompanying solution provided for grading and reproducibility. - Spark SQL Schema Generator Utility (SparkUtils.generate_schema): Python utility to build Spark StructType schemas from column name-type pairs with usage example, simplifying schema creation. - Data Loading and Consolidated Rentals Analytics: data ingestion from multiple datasets (agencies, brands, cars, customers, rentals), JSON field extraction, and inner joins to produce a consolidated rental view (car, agency, customer). Major bugs fixed: - No explicit bugs reported in this period; focus was on feature delivery and tooling enhancements. If any minor issues were identified, they were addressed within the respective commits and refactors. Overall impact and accomplishments: - Delivered end-to-end lab materials and a reusable analytics pipeline, enabling students to work with real Spark datasets and produce a consolidated rentals view, which supports product insights and decision-making. - Established reusable tooling (SparkUtils) to streamline schema creation, reducing setup time and potential schema drift in future projects. - Improved reproducibility and onboarding for data engineering tasks across the course, aligning with academic and business goals. Technologies/skills demonstrated: - PySpark / Spark SQL, Python utilities, and data engineering best practices - Data cleaning, normalization, null handling, feature engineering - JSON field extraction and multi-dataset joins - Schema design with Spark StructType and programmatic schema generation - Emphasis on business value: faster student onboarding, scalable analytics, and reliable data schemas.
September 2025
6 Commits • 4 Features
Sep 1, 2025September 2025 monthly summary for pcamarillor/O2025_ESI3914B: Key features delivered: - Course Lab Notebooks for Autumn 2025 (Lab 02 and Lab 04): user-facing lab notebooks and Spark environment setup to accelerate student onboarding and hands-on practice. - Lab 03 Notebook and Solution (Data Cleaning and Feature Engineering on Flight Data): end-to-end notebook for data cleaning, normalization, null handling, and feature engineering; accompanying solution provided for grading and reproducibility. - Spark SQL Schema Generator Utility (SparkUtils.generate_schema): Python utility to build Spark StructType schemas from column name-type pairs with usage example, simplifying schema creation. - Data Loading and Consolidated Rentals Analytics: data ingestion from multiple datasets (agencies, brands, cars, customers, rentals), JSON field extraction, and inner joins to produce a consolidated rental view (car, agency, customer). Major bugs fixed: - No explicit bugs reported in this period; focus was on feature delivery and tooling enhancements. If any minor issues were identified, they were addressed within the respective commits and refactors. Overall impact and accomplishments: - Delivered end-to-end lab materials and a reusable analytics pipeline, enabling students to work with real Spark datasets and produce a consolidated rentals view, which supports product insights and decision-making. - Established reusable tooling (SparkUtils) to streamline schema creation, reducing setup time and potential schema drift in future projects. - Improved reproducibility and onboarding for data engineering tasks across the course, aligning with academic and business goals. Technologies/skills demonstrated: - PySpark / Spark SQL, Python utilities, and data engineering best practices - Data cleaning, normalization, null handling, feature engineering - JSON field extraction and multi-dataset joins - Schema design with Spark StructType and programmatic schema generation - Emphasis on business value: faster student onboarding, scalable analytics, and reliable data schemas.
Activity
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Quality Metrics
Correctness80.0%
Maintainability85.0%
Architecture82.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonSQLShell
Technical Skills
Apache SparkBig Data ProcessingData AnalysisData CleaningData EngineeringData ProcessingData TransformationDocumentationETLGraph DatabasesJupyter NotebooksLab Notebook CreationNeo4jPySparkSchema Definition
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