The technology and software on the internet keep progressing, all thanks to the accelerated movement of information. Knowledge helps us create, develop, improve products, digital projects, and even more complex entities like entire business models.
The primitive and limited ways of obtaining and storing information in the past are the primary factors that stopped humanity from evolving faster. Even the parties with superior knowledge and resources have used them to dominate their niches and reduce the chances for new competitors.
New ways of data collection and storage gave new opportunities for talented groups and individuals to access knowledge and contribute to new products and inventions.
The digital revolution has given us the biggest leap in data distribution, and with it – our biggest advancements. Today, we have more information than our brain can process, all thanks to the internet. When everyone has access to so much public data, the resource deficit is much smaller for new inventors and business people, creating a much more competitive landscape that drives progress. When knowledge is available for everyone, talented individuals get recognized for new ideas.
When businesses and startups are so competitive, and public information is so accessible, who gets the bigger slice of the pie? It all comes down to efficient data extraction and analysis. When you have everything to aggregate data faster, you still need technology for proper analysis. With the potential tools at our disposal, manual browsing and collection will not suffice.
In this article, we will talk about the technology of web scraping and data parsing because they allow us to both extract information and rearrange it into an understandable format. By familiarizing yourself with these tools, you will understand the most efficient ways of obtaining data and converting it into knowledge, with possibilities to apply web scrapers and parsers for your business or personal projects. While web scraping has a lot of leeway for automated data extraction, organizing aggregated information often comes with many parsing errors. We will help you understand the basic technology behind these processes, teach you to use and test these tools for yourself, and potential parsing errors for you to recognize and fix in your projects.
How web scraping works
Web scraping technology allows us to aggregate information with inhuman efficiency. Instead of connecting to a targeted server via browser, we use automatable bots and open-source programming frameworks to accelerate this process.
Bigger scrapers have many features to tailor data extraction to every situation, but you can also customize your coded scrapers to locate and select only the information you need.
You can use web scraping to automate and accelerate information extraction for different, not necessarily tech-related projects. Learning to collect data at a far greater pace is a very versatile skill.
Modern businesses need much more information to outperform or at least maintain their success relative to the competition. A company can use employees to aggregate knowledge about the market and its players, but one web scraper, let alone multiple scrapers working simultaneously, can extract the same information in a fraction of time.
The efficiency of web scrapers depends on their settings, but faster is not always better. If your bot sends too many data packets to a targeted server, it can be identified as a non-human visitor. To avoid unnecessary load, website owners ban IP addresses of suspicious visitors to protect public data from bots and preserve a stable connection to the page. While an IP ban is already frustrating for a private user, businesses want to avoid exposing their network identity at all costs. That is why, web scrapers usually connect to their targets through residential proxies – middlemen servers that help you preserve anonymity online. Legitimate providers have a pool of proxy IPs that help businesses avoid IP blacklisting and continue scraping. While you probably won’t need a proxy server for scraping tests and personal projects, we recommend familiarizing yourself with the service before the tasks get more complex.
The importance of data parsing
When we collect public data manually, the process is much slower, but we can store the information in the desired format. When we use web scrapers for data extraction, we get a line of code designed for browsers. To detangle this knowledge and make it usable, we need data parsing.
The functionality and complexity of a parser depend on the programming language and its instructions. For larger scraping operations, companies build their own scrapers and maintain them to adapt to every situation without parsing errors.
Working on parsers is a good coding experience for junior programmers, but it is a tedious task. Due to many unpredictable factors, even the best scrapers require adjustments to avoid parsing errors. The concept is simple – you submit your input (usually in HTML) to receive data in a readable and analyzable form but different dynamic web page structures can make your data parsers obsolete. Parsing remains a tedious but necessary part of the information aggregation experience because, by its nature, it does not easily submit to automation.
Businesses that rely on data aggregation
If you are interested in manipulating the technology for data extraction, we recommend pursuing a career in data science. To simplify modernization for other companies, the demand for web scraping and data parsing services is high. We can already observe an abundance of aggregator companies that focus on helping others with their data extraction tasks. Travel tickets, real estate prices, competitor prices – anyone can benefit from the technology of web scraping!