📅 2025-07-07 — Session: Refactored Data Processing Pipeline and Integrated Scraper
🕒 05:40–06:25
🏷️ Labels: Pipeline, Debugging, Python, Automation, Orchestrator
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary aim of this session was to debug, refine, and integrate various components of a data processing pipeline using Python, with a focus on improving efficiency and compatibility.
Key Activities
- Pipeline Debugging: Addressed column name mismatches and directory inconsistencies in data processing scripts to ensure smooth pipeline execution.
- Error Handling: Implemented code changes to prevent the creation of empty CSV files when no SERP results are found.
- Integration: Merged a JSONL exporter with a Selenium-based scraper to enrich data with HTML content.
- Export Process Update: Modified the export process to utilize the integrated scraper and exporter for JSONL data.
- Workflow Optimization: Refined job scraping workflow to prioritize classification before scraping, enhancing efficiency.
- Modular Orchestrator Design: Redesigned the orchestrator for the data pipeline to improve flexibility and traceability.
- Script Compatibility: Enhanced script compatibility with the orchestrator pattern by implementing an
argparseinterface. - Pipeline Integration: Integrated a JSONL export step for classifying SERP URLs, updating the orchestrator accordingly.
Achievements
- Successfully debugged and fixed issues in the data processing pipeline.
- Enhanced error handling to prevent empty CSV creations.
- Achieved seamless integration of the JSONL exporter with the Selenium scraper.
- Improved the efficiency of the job scraping workflow.
- Developed a modular orchestrator design, facilitating better management and debugging.
- Updated scripts to be compatible with the orchestrator pattern.
Pending Tasks
- Further testing of the integrated pipeline to ensure all components work harmoniously.
- Continuous monitoring and optimization of the job scraping workflow for better performance.