What method is used to crawl big data in Python?

Python offers various methods for web scraping big data, including the following commonly used options:

1. Utilizing third-party libraries: Python offers a variety of powerful third-party libraries such as BeautifulSoup and Scrapy, which can assist in web scraping. These libraries provide extensive functionalities and APIs, allowing for automated web parsing and data extraction.
2. Many websites and services offer API interfaces which allow data access and extraction using the Python programming language. You can utilize Python’s request library (such as requests) to send HTTP requests and retrieve data.
3. Utilizing a web crawling framework: Python’s Scrapy framework is a powerful web crawling tool that offers highly customizable crawling processes and data processing capabilities. By using Scrapy, efficient concurrent crawling and data extraction can be achieved.
4. Using databases: When scraping a large amount of data, you can save it to a database using Python’s database interface (such as SQLite, MySQL, MongoDB, etc.). Then, you can use SQL queries to filter and extract the data needed.
5. Implement parallel processing: To efficiently scrape large amounts of data, Python’s parallel processing libraries (such as multiprocessing, concurrent.futures, etc.) can be used to simultaneously execute multiple tasks in order to improve crawling speed and efficiency.

Please be aware that when conducting large-scale data scraping, it is important to comply with the rules and policies of the website to avoid placing too much burden on the server or violating the privacy rights of others.