Building a Cloud-Based Web Scraping Automation System

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Building a Cloud-Based Web Scraping Automation System

Setting up an efficient web scraping architecture that runs automatically in the cloud can significantly streamline data collection processes. A recent implementation shows how to create a system that runs scripts every 15 minutes without requiring constant management.

The architecture uses Python as its foundation and employs a monorepo approach rather than separate microservices. This design choice simplifies deployment and maintenance compared to alternative approaches that would require setting up multiple cloud functions, pub/sub subscriptions, and IAM permissions for each component.

Task Scheduling Implementation

The system implements a task scheduler using a hash map structure that organizes jobs based on 24-hour time slots. For example:

  • 1800 hours (6:00 PM) – First set of tasks
  • 1815 hours (6:15 PM) – Second set of tasks
  • 1830 hours (6:30 PM) – Third set of tasks
  • 1845 hours (6:45 PM) – Fourth set of tasks

This scheduling system makes it easy to assign different scraping and processing tasks to specific time slots throughout the day.

Concurrent Processing with ThreadPoolExecutor

A key optimization in the system is the use of Python’s concurrent.futures ThreadPoolExecutor. This approach is particularly effective for I/O-bound operations like API calls and web scraping, where the program spends most of its time waiting for external responses.

Instead of making sequential API calls and waiting for each response before proceeding to the next task, ThreadPoolExecutor allows multiple requests to be sent simultaneously. Workers process these tasks concurrently, significantly reducing the total execution time.

Practical Applications

The system currently handles several automated tasks:

  • Daily market updates at 7:00 AM for US markets, emerging markets, China markets, and developed markets (excluding North America)
  • Automatic web scraping of earnings calls
  • Summarization of earnings call content

The earnings call summaries are particularly useful, as they extract key information such as revenue figures, EPS, management changes, and strategic decisions from lengthy transcripts. This transforms hours of reading into concise, structured data that can be quickly reviewed.

Future Improvements

Several enhancements are planned for the system:

  • Improving the markdown formatting of the earnings call summaries for better readability
  • Collecting and storing the prompts used to generate summaries to enable iterative improvements
  • Adding an AI chat feature to allow users to ask questions about the collected data
  • Creating a community chat feature for users to discuss insights
  • Fine-tuning the timing of script execution to account for timezone differences and data availability

By automating these web scraping and data processing tasks, the system demonstrates how to efficiently collect and analyze information that would be impractical to process manually.

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