Enhanced Precios Claros Scraping Pipeline

📅 2024-10-28 — Session: Enhanced Precios Claros Scraping Pipeline

🕒 16:45–17:45
🏷️ Labels: Scraping, Automation, Data_Pipeline, Debugging, Cloud_Infrastructure
📂 Project: Dev
⭐ Priority: MEDIUM

The session aimed to enhance the Precios Claros scraping pipeline to improve efficiency in capturing and storing daily price data.

Refined and optimized the scraping pipeline with steps for automation, data consolidation, duplicate handling, and documentation.
Utilized Unix grep commands to filter command history for scraping-related commands, focusing on scrapy and shub.
Developed a debug-friendly scraping command using Scrapy for efficient debugging with specific store IDs.
Employed ipdb for debugging Python scripts by inspecting variables and continuing execution.
Set up an automated scraper using cloud infrastructure with error handling and long-term maintenance strategies.
Configured a cost-effective server on Google Cloud Platform for running web scrapers, including VM configuration and scheduling.
Analyzed recent scraping job results and proposed next steps for automation and data management enhancements.
Automated CSV management for price data using a Python script (consolidar_precios.py) for efficient consolidation and historical data storage.
Optimized daily ETL processes for time-series price data using advanced techniques like Change Data Capture and Delta Encoding.
Proposed a lightweight ETL process using Pandas for efficient management of price data changes.
Structured multiple Scrapy spiders execution from a Jupyter notebook in VS Code, processing data with Pandas.
Executed sequential Scrapy spiders for an ETL pipeline using Bash commands.
Provided instructions for using ipdb in non-cursor environments like Jupyter and VS Code.

Successfully optimized the Precios Claros scraping pipeline and improved automation and data management processes.
Enhanced debugging capabilities using ipdb and improved server setup on GCP.