Enhanced Election Data Pipeline with Logging

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📅 2025-10-26 — Session: Enhanced Election Data Pipeline with Logging

🕒 15:00–16:10
🏷️ Labels: Python, Data Processing, Election Data, Logging, Pipeline, Error Handling
📂 Project: Dev

Session Goal

The session aimed to enhance the election data processing pipeline by implementing deterministic data normalization, robust error handling, and structured logging.

Key Activities

  1. Data Normalization Script: Developed a Python script to normalize election data with deterministic behavior, ensuring schema compliance and loud error handling.
  2. Dimension Table Script: Created a script to build dimension tables from normalized CSV data, ensuring name harmonization and ID stability.
  3. Election Facts Script: Designed a script to process election data into facts, enforcing data integrity and optional Parquet partitioning.
  4. Pipeline Contract Enhancement: Reviewed and recommended improvements for pipeline contracts, focusing on configuration and normalization steps.
  5. Ingestion and Extraction Improvements: Enhanced data ingestion and extraction processes with explicit schema definitions and provenance tracking.
  6. CSV Ingestion Script: Developed a robust script for ingesting and deduplicating election result CSVs.
  7. Pipeline Scripts Overview: Outlined a series of scripts for pipeline automation, detailing commands and validation checks.
  8. Structured Logging Implementation: Implemented structured logging for file ingestion processes, handling duplicates and maintaining a manifest.
  9. Consistent Logging Setup: Established a consistent logging setup across Python pipelines using a YAML configuration.
  10. Package Import Path Solutions: Explored solutions for package import path issues in Python scripts.

Achievements

  • Successfully implemented deterministic normalization and robust error handling in data processing scripts.
  • Enhanced the reliability and integrity of the election data pipeline with structured logging and improved contracts.

Pending Tasks

  • Further testing of the logging setup in diverse pipeline scenarios.
  • Review and optimization of package import paths for broader compatibility.

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