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
Axel Escoto developed a suite of data engineering features in the pcamarillor/O2025_ESI3914B repository, focusing on hands-on lab notebooks, analytics pipelines, and real-time data workflows. He built end-to-end solutions for data cleaning, schema generation, and consolidated analytics using PySpark and SQL, enabling students to work with real Spark datasets and streamline onboarding. Axel also implemented a Neo4j graph ingestion pipeline and a real-time log analysis workflow with Structured Streaming, demonstrating expertise in big data processing and graph databases. His work emphasized reproducibility, documentation, and reusable tooling, delivering depth in both technical implementation and educational value.
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 people
Work 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
Repositories Contributed To
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