Execution and Debugging of Data Processing Pipelines

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📅 2025-07-07 — Session: Execution and Debugging of Data Processing Pipelines

🕒 04:25–05:25
🏷️ Labels: Automation, Pipeline, Debugging, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to execute and debug various components of data processing pipelines, focusing on automation and error resolution.

Key Activities

  • Developed a strategic execution plan for scrappy agents with user configuration, automation, and monetization strategies.
  • Provided strategic recommendations for productivity agents, including a Streamlit UI template for configuration uploads.
  • Outlined a mid-sprint sanity plan for maintaining productivity during intense work periods.
  • Reviewed the job search pipeline status and outlined next steps.
  • Designed and executed the run_full_pipeline.py script for job data processing.
  • Fixed argument mismatch in the 01_serp_scraper.py script.
  • Provided Bash commands for file management.
  • Resolved a JSONL conversion error and diagnosed a JSONL output name mismatch.
  • Debugged file path issues and enhanced subprocess command for live output.
  • Improved error handling in the CSV processing pipeline.

Achievements

  • Successfully implemented fixes for argument mismatches and JSONL conversion errors.
  • Enhanced real-time logging and debugging capabilities.
  • Improved robustness of CSV processing pipeline.

Pending Tasks

  • Further standardize naming patterns in data pipeline scripts to ensure compatibility with downstream processing.

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