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In this article we discuss some main challenges that e-commerce retailers face on a daily basis due to the amount of web data needed and how to solve them.

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.

I recently had the pleasure of participating in the third episode of Graphversation, a monthly live stream series that brings together graph experts and Neo4j enthusiasts for engaging and enlightening discussions about the captivating world of graphs.

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.

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’?