The Internet is the crown jewel of information technologies. After years of transformation in IT and telecommunication, we have a massive and adaptive network of interconnected devices with differing computational power but capable of fast digital data transmission.
Through decades of partnerships and teamwork through information exchange and manipulation, we have information systems branching out into various forms of knowledge, visual aid, and entertainment – all stemming from a beautiful marriage of godlike, always-improving hardware and multifunctional software.
An underlooked advantage of IT is a base for modern efficiency and comfort – digital data storage. The ever-evolving transmission tools and size of stored information are the base of the web, and some advancements create massive bundles of data – too large for human comprehension. Publicly available information, whose fragments, when fragmented and displayed on user interfaces, only present a part of knowledge to the visitor, can be filtered into big sets, ready for analysis.
In this article, we will discuss the tools to tackle big data – data scraping bots. Our goal is to provide an introduction to these wonderful tools that extract and filter and analyze information, transforming it into readable and understandable data sets whose use cases can only be constrained by your imagination. Web scrapers send many data requests. You will need to learn about internet privacy tools to protect information extraction tasks. For example, with an India proxy, you can web scrape local websites without location blocking and exposure of your identity. You can read more about them on blogs written by Smartproxy – one of the top industry providers, and get your first India proxy for an affordable price!
How to start data scraping
The beauty of web scraping bots is their simplicity. Even without prior programming knowledge you can find and inspect Python codes or watch tutorials to write simple data extraction scripts. While there are scrapers written in other languages, such an introduction is your best bet if you lack experience.
When starting data scraping, start small, do not get ahead of yourself by trying to replicate the features of complex data extraction bots, and focus on safe targets. By safe, we mean pages like Wikipedia that offer a lot of information, variety for filtering, and performance management without the threat of retaliation.
If you decide to pursue a career in data science or need data scraping to assist your business tasks, your future targets may not be so kind. Big companies, retailers, social media platforms, and search engines are the richest sources of valuable public information but they protect web servers from the extra load of bots in an attempt to guarantee the highest level of real user engagement. Nobody likes scrapers because they skew the data on user behavior which is cherished as a collection of valuable parameters that help improve the website and its primary goals. However, because public data scraping is perfectly legal, modern businesses are in a constant battle of extraction, protection, and deception.
How web scrapers work
Most modern scrapers are designed to serve two purposes – extract the HTML code and parse the information into a readable and understandable format. Even the most basic scripts need to utilize a parsing library to get real value from aggregated data.
While downloading the desired page is simple enough, parsing does not always come easy, and the degree of difficulty depends on the target website structure. When working on many targets at the same time, parsers are nearly impossible to automate, making the part of the data extraction process that requires the most human resources – workers that can adjust the tool for complete and efficient knowledge acquisition.
Data scraping problems
Aside from the difficulties of data parsing, the main issues that stop or slow down data extraction are privacy vulnerabilities. If scraping bots collect information by sending data requests from your IP address, competitors can recognize the automated traffic and blacklist your identity.
The likelihood of IP bans increases tenfold when trying to accelerate web scraping through scalability. One bot that bombards the recipient server with a request already raises enough suspicion, but most companies want to use multiple data scrapers at the same time.
Thankfully, all the danger is cast aside with proxy servers – the intermediary servers that help the process on many levels.
First of all, the best providers offer residential proxies – the best addresses from data scraping because they come from devices with authentic IPs from internet service providers. With a large assortment of quality addresses, you can give each data scraper a different address and enable rotating options to defragment your connections.
Proxy servers are essential when scraping web servers in other regions, especially websites restricted to local users. The best residential proxy providers have addresses in most countries in the world. A good supplier will offer an affordable deal for IPs in the desired region.
Web scraping bots and scripts may not charm you with complexity, but they provide simple, automated solutions with plenty of breathing room for additional features and performance boosts. When paired with quality residential proxies, they are unstoppable tools for fast and efficient data acquisition, restructuring, and analysis.
