BigData2025-Rev/p3
Jan 2025 – Feb 2025
2 Months active
Languages Used
PythonSQLMarkdowngitignore
Technical Skills
Big DataConfiguration ManagementData CleaningData EngineeringData LoadingData Processing
miguelpena-bigdata-dev
PROFILE
Miguelpena-bigdata-dev
Miguel developed a robust data pipeline and national population analysis application for the BigData2025-Rev/p3 repository, focusing on scalable data engineering and analytics workflows. He established a modular project structure in Python and PySpark, implementing data cleaning, multi-source HDFS ingestion, and ORC file processing to ensure consistent, production-ready pipelines. His work included composite key generation, decade-aware transformations, and integration of metro status metrics to enrich urbanization analysis. By exporting Power BI-ready CSVs and supporting regression analysis, Miguel enabled streamlined data visualization and decision-making. He also improved code organization, observability, and documentation, demonstrating depth in data processing and maintainability.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
30Total
Bugs
1
Commits
30
Features
7
Lines of code
728
Activity Months2
Your Network
16 people
Work History
February 2025
14 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for BigData2025-Rev/p3. Key features delivered: - National population data analysis app with Power BI integration: Standalone workflow with modules to load data from HDFS, compute national totals, and manage Spark configurations. Processes ORC files and exports a single CSV ready for Power BI import, with an accompanying Power BI visualization file and baseline regression results to support trend analysis. Major bugs fixed: - Fixed missing summary levels 40/50 for 2010 and 2020 by casting SUMLEV to IntegerType before filtering, improving data completeness and reliability. Additional improvements: - Added metro status calculation to the data cleaning pipeline to enrich urbanization metrics. - Code cleanup and observability enhancements: removed debug statements, improved data schema visibility, restructured analysis folder, fixed corrupted local objects, and expanded documentation. Impact and accomplishments: - Accelerated delivery of Power BI-ready data assets, improved data quality and reliability, and strengthened pipeline maintainability, enabling faster, data-driven decision-making. Technologies/skills demonstrated: - PySpark / Spark configurations, HDFS data ingestion, ORC handling, data transformations, basic regression analysis, Power BI integration, data cleaning pipelines, observability and code hygiene.
14 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for BigData2025-Rev/p3. Key features delivered: - National population data analysis app with Power BI integration: Standalone workflow with modules to load data from HDFS, compute national totals, and manage Spark configurations. Processes ORC files and exports a single CSV ready for Power BI import, with an accompanying Power BI visualization file and baseline regression results to support trend analysis. Major bugs fixed: - Fixed missing summary levels 40/50 for 2010 and 2020 by casting SUMLEV to IntegerType before filtering, improving data completeness and reliability. Additional improvements: - Added metro status calculation to the data cleaning pipeline to enrich urbanization metrics. - Code cleanup and observability enhancements: removed debug statements, improved data schema visibility, restructured analysis folder, fixed corrupted local objects, and expanded documentation. Impact and accomplishments: - Accelerated delivery of Power BI-ready data assets, improved data quality and reliability, and strengthened pipeline maintainability, enabling faster, data-driven decision-making. Technologies/skills demonstrated: - PySpark / Spark configurations, HDFS data ingestion, ORC handling, data transformations, basic regression analysis, Power BI integration, data cleaning pipelines, observability and code hygiene.
February 2025
January 2025
16 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for BigData2025-Rev/p3: Delivered a robust data pipeline foundation and feature set across multiple data sources, with enhanced lineage, decoupled components, and readiness for production-grade pipelines. Achieved significant improvements in data consistency, traceability, and processing flexibility, laying groundwork for analytics and decision-making.
January 2025
16 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for BigData2025-Rev/p3: Delivered a robust data pipeline foundation and feature set across multiple data sources, with enhanced lineage, decoupled components, and readiness for production-grade pipelines. Achieved significant improvements in data consistency, traceability, and processing flexibility, laying groundwork for analytics and decision-making.
Activity
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Quality Metrics
Correctness82.4%
Maintainability84.0%
Architecture78.0%
Performance73.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQLgitignore
Technical Skills
Big DataCode OrganizationCode RefactoringConfiguration ManagementData AnalysisData CleaningData EngineeringData LoadingData ProcessingData TransformationData VisualizationDebuggingDocumentationETLFile Handling
Repositories Contributed To
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