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cURL simplifies data collection from websites via its command-line interface, making it essential for APIs, file transfers, and web scraping.

Imagine a long crawling process, like extracting data from a website for a whole month. We can start it and leave it running until we get the results.

HTML tables are a very common format for displaying information. When building scrapers you often need to extract data from HTML tables on web pages and turn it into some different structured format, for example, JSON, CSV, or Excel. In this article, we discuss how to extract data from HTML tables using Python and Scrapy.

Web crawlers are becoming increasingly popular in the era of big data, especially now with the advent of Large Language Models (LLMs) such as ChatGPT and LLaMA. The sheer amount of data that is publicly available from the web has a wide variety of applications including market research, sentiment analysis, and predictive modeling.

Much is said about quality assurance and the automated data QA process. But do you really know how to get around doing it in the right way?

For the best results from your data extraction campaign, it's important to know how to carry out web scraping without being blocked.

If you are interested in web scraping as a hobby or you might already have a few scripts extracting data but are not familiar with Scrapy then this article is meant for you.

It's a 21st-century truism that web data touches virtually every aspect of our daily lives. We create, consume, and interact with it while we’re working, shopping, traveling, and relaxing. It’s not surprising that web data makes the difference for companies to innovate and get ahead of their competitors. But how to extract data from a website? And what’s this thing called ‘web scraping’?

If you haven’t read the previous parts of our Practical guide to web data QA, here are the first part, second part, third part and fourth part of the series.