Refactored Data Processing Pipeline and Error Resolution

📅 2025-06-22 — Session: Refactored Data Processing Pipeline and Error Resolution

🕒 01:50–03:00
🏷️ Labels: Data_Processing, Merge_Logic, Python, Pandas, Error_Resolution
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the data processing pipeline by improving merge logic, reconstructing RSS indices, and resolving key generation errors in Python scripts.

Key Activities

• Improved Merge Logic: Enhanced the merge logic in data processing scripts by using a unique identifier (index_id) instead of ambiguous titles, ensuring data quality and integrity.
• Reconstructed RSS Index: Developed a method to rebuild the rss_index from the master_ref.csv, ensuring accurate data retrieval using unique identifiers.
• Code Review: Conducted a detailed review of the rss_index and article_key construction, addressing ambiguity issues and ensuring key compatibility.
• Error Diagnosis: Diagnosed and solved a DataFrame error related to the missing ‘index_id’ column, proposing a robust solution to check for its existence before merging.
• Pipeline Refactoring: Suggested refactoring of the data enrichment pipeline to separate responsibilities and eliminate code duplication.
• Error Resolution: Addressed type errors in DataFrame key generation by ensuring type consistency during concatenation.
• Conflict Resolution: Solved naming conflicts in DataFrame merges by ensuring the correct generation of ‘index_id’ and implementing defensive checks and data cleaning.

Achievements

• Successfully refactored the data processing pipeline, improving efficiency and reducing errors.
• Enhanced data quality by resolving key generation and merge logic issues.
• Improved code maintainability through refactoring and detailed code reviews.

Pending Tasks

• Further testing of the refactored pipeline to ensure robustness in different data scenarios.
• Implementation of additional defensive checks in data processing scripts to prevent future errors.