Table of contents
Scraping Practices and Technologies used at Web Scraping.
At webscraping.net, we believe in cutting through complexity. Here’s what drives our web scraping projects:
Python: The Backbone
Python is our bedrock, providing power and simplicity for seamless web scraping and data manipulations.
Scrapy: Navigating with Ease
We rely on Scrapy, a Python web scraping framework for smooth data collection from the Internet.
Redis: Handling Scale
For larger distributed projects, we turn to Redis, an in-memory data store that keeps things running efficiently.
Playwright: JavaScript rendering
While we try to avoid JS rendering, for dynamic websites, we harness the power of Playwright, a JavaScript library that enables browser automation and rendering. It allows us to interact with websites as if a real user were browsing, ensuring we capture data from JavaScript-rendered pages.
Data Storage: Your Choice
Our mainstay is PostgreSQL, a robust open-source database.
We have also worked with:
- MySQL, MariaDB: Performance-focused databases.
- Amazon RDS, Amazon S3: Cloud storage options.
- Airtable, Google Sheets: Collaborative tools.
- Google Cloud Storage: Integrated solution.
- ClickHouse: Fast and scalable analytical database.
Simplicity, Meets Efficiency
Our tech stack is all about making web scraping simple and effective.
