
Worked on the BigData2025-Rev/p3 repository to deliver geographic data enrichment and metro-status categorization, enabling more granular geographic segmentation and reliable analytics. Developed a Metro_Status column derived from MACCI values and established metro, region, and urban-rural classifications within the data pipeline. Enhanced the DataCleaner component by refining column renaming, improving null value management, and expanding documentation for maintainability. Updated the data loader to expose new geographic columns, ensuring consistency from ingestion through analytics. Leveraged Python, PySpark, and ETL skills to improve data quality, support targeted business insights, and reduce maintenance risk through explicit method descriptions and robust data handling.
January 2025 (2025-01) focused on delivering geographic data enrichment and metro-status categorization in BigData2025-Rev/p3, enabling finer geographic segmentation and more reliable downstream analytics. Key work includes adding a Metro_Status column derived from MACCI, establishing metro/region/urban-rural classifications, and hardening DataCleaner with enhanced geographic data handling (column renaming, null value management, and expanded docstrings). The data loader was updated to expose the new geographic columns, ensuring end-to-end consistency from ingestion to analytics. This work improves data quality, enables targeted business insights, and reduces maintenance risk through added documentation and explicit method descriptions. Commits supporting this work include: MACCI value simplification, new column integration into the data loader, improved column calling and null handling, and enhanced method documentation.
January 2025 (2025-01) focused on delivering geographic data enrichment and metro-status categorization in BigData2025-Rev/p3, enabling finer geographic segmentation and more reliable downstream analytics. Key work includes adding a Metro_Status column derived from MACCI, establishing metro/region/urban-rural classifications, and hardening DataCleaner with enhanced geographic data handling (column renaming, null value management, and expanded docstrings). The data loader was updated to expose the new geographic columns, ensuring end-to-end consistency from ingestion to analytics. This work improves data quality, enables targeted business insights, and reduces maintenance risk through added documentation and explicit method descriptions. Commits supporting this work include: MACCI value simplification, new column integration into the data loader, improved column calling and null handling, and enhanced method documentation.

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