![]() ![]() Since the process is continuous, you will end up with huge amounts of data. Extracting data from several websites translates into thousands of web pages. Large scale operations come with high storage capacity requirements. The deliverable of data extraction scripts is data. Servers are a must-have as they allow you to run your previously written scripts 24/7 and streamline data recording and storing. So the next step in this process is investing in server infrastructure or renting servers from an established company. To continually run your web scrapers, you need a server. This will also put less strain on your servers, reduce storage space requirements, and make data processing easier. ![]() There is no need to extract everything when you can specifically target just the data you need. The data you need to get extracted depends on your business goals and objectives. Scripts that are used to extract data can be custom-tailored to extract data from only specific HTML elements. Developing various data crawling patterns They send a request to a server, visit the chosen URL, go through every previously defined page, HTML tag, and components. These scripts can scrape data in an automated way. Python advantages such as diverse libraries, simplicity, and active community make it the most popular programming language for writing web scraping scripts. Programmers skilled in programming languages like Python can develop web data extraction scripts, so-called scraper bots. Developers are able to come up with scripts that pull data from any manner of data structures. It defines the structure of the website’s content via various components, including tags such as, , and. Nowadays, the data we scrape is mostly represented in HTML, a text-based mark-up language. Now, we will discuss the whole process to fully understand how to extract web data. ![]() For this reason, we have covered this issue in our other blog post about the main differences between web crawling and web scraping. Sometimes the concept of web scraping is confused with web crawling. The term typically refers to an automated process that is created with intention to extract data using a bot or a web crawler. Sometimes you can find it referred to as web harvesting as well. The process of extracting data from websites is called web scraping. ![]() However, it is not that complicated to comprehend the entire process. If you are a not-that-tech-savvy person, understanding how to extract data can seem like a very complex and incomprehensible matter. For this reason, in this article, we shall go through how web data extraction works, its main challenges, and introduce you to several solutions that can help you as you go further up the data scraping path. However, this is not one of those processes that you can implement in your day to day operations before getting informed. It has become common for various companies to extract data for their business purposes. Fortunately, there is a lot of public data stored on servers across websites that can help businesses to stay sharp in the competitive market. To fuel these decisions, companies track, monitor, and record relevant data 24/7. We live in an era when making data-driven business decisions is the number one priority for many companies. ![]()
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