
Over a two-month period, contributed to the BigData2025-Rev/p3 repository by building scalable data engineering pipelines and analytics workflows for redistricting and population growth analysis. Developed end-to-end processes using Python, pandas, and PySpark to automate extraction, transformation, and consolidation of state-level datasets, converting them into ORC format for efficient storage and analysis. Enhanced data quality through robust deduplication and modularized merge logic, while maintaining repository hygiene. Delivered district-level population growth analytics for multiple census years, exporting results to CSV and creating Power BI visualizations with comprehensive documentation to support stakeholder interpretation and downstream usage of the processed data.
February 2025 monthly summary for BigData2025-Rev/p3: Delivered an end-to-end district-level population growth analysis and visualization pipeline, leveraging PySpark on ORC data for years 2000, 2010, and 2020. Implemented population counts and growth rates for adult and youth demographics, exported results to CSV, and developed Power BI visualizations for stakeholders. Produced comprehensive documentation including a context-question file to guide interpretation. Three commits supported the delivery, culminating in final Spark analysis code and Power BI reports.
February 2025 monthly summary for BigData2025-Rev/p3: Delivered an end-to-end district-level population growth analysis and visualization pipeline, leveraging PySpark on ORC data for years 2000, 2010, and 2020. Implemented population counts and growth rates for adult and youth demographics, exported results to CSV, and developed Power BI visualizations for stakeholders. Produced comprehensive documentation including a context-question file to guide interpretation. Three commits supported the delivery, culminating in final Spark analysis code and Power BI reports.
January 2025 performance summary for BigData2025-Rev/p3: Delivered two end-to-end data pipelines for redistricting data, improved data quality through robust deduplication during merges, and completed repository hygiene improvements. The work establishes a scalable, unified data layer for cross-state analyses, leveraging Python (pandas) and PySpark with ORC storage to optimize analytics workflows.
January 2025 performance summary for BigData2025-Rev/p3: Delivered two end-to-end data pipelines for redistricting data, improved data quality through robust deduplication during merges, and completed repository hygiene improvements. The work establishes a scalable, unified data layer for cross-state analyses, leveraging Python (pandas) and PySpark with ORC storage to optimize analytics workflows.

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