πŸ“… 2025-07-07 β€” Session: Enhanced Data Processing Pipeline

πŸ•’ 05:35–06:25
🏷️ Labels: Data_Pipeline, Automation, Python, Error_Handling, Integration
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal

To improve and integrate various components of a data processing pipeline, ensuring robustness, compatibility, and efficiency.

Key Activities

  • Pipeline Fixes: Addressed column name mismatches and directory misalignments to ensure smooth script execution.
  • Error Handling: Implemented strategies to prevent the creation of empty CSV files, modifying three scripts for better error handling.
  • Integration: Merged a JSONL exporter with a Selenium scraper, allowing for seamless data export without overwriting existing files.
  • Workflow Refinement: Transitioned job scraping and classification to a selective model, improving efficiency by scraping only high-quality job postings.
  • Orchestrator Design: Developed a modular orchestrator for pipeline execution, transforming existing scripts into a flexible driver with configurable paths.
  • Script Enhancement: Updated the 02_label_and_score.py script for orchestrator compatibility using argparse and functional wrapping.
  • Pipeline Integration: Integrated a JSONL export step, updating the orchestrator’s workflow to convert CSV data into JSONL format.

Achievements

  • Successfully integrated and enhanced multiple components of the data processing pipeline.
  • Improved error handling and compatibility across scripts.

Pending Tasks

  • Further testing and validation of the integrated pipeline components.
  • Exploration of additional enhancements for future scalability.