The modern business environment is dominated by the pursuit of public information. With the magic of the internet connecting people all around the world, it is an endless mine of valuable data. Easy access to valuable knowledge creates great opportunities for education and innovation.
While the abundance of data brings educational content, entertainment, and various convenient tools into our lives, it creates unique modern problems. When everyone can find the necessary information in a blink of an eye, the majority of internet users, especially younger generations, tend to do the bare minimum and choose the path of least resistance. Ironically, additional opportunities often overwhelm internet users and make us lazy, and addictive entertainment platforms do not help.
Fortunately, for driven and talented individuals, the free flow of information is a blessing that contributes to exponential growth and innovation. However, the self-replenishing mine of data creates new challenges. Because no human can manually collect and process so much knowledge, we rely on technology to segmentize, improve, and accelerate these tasks. Collected data helps us make precise business decisions and fuel machine learning, so we need efficient ways to extract, organize, and store information.
In this article, we will introduce the concept of data parsing to a non-tech-savvy audience. While a human brain is good at multitasking and automatically turns acquired information into knowledge, automation helps us achieve these goals with far greater efficiency. However, machines and software cannot perform all tasks at once. Data extraction starts with web scraping, but this step only gives us aggregated code. Data parsing helps us convert it into a readable and understandable format, suitable for further analysis. If you want more detailed information about the information, look up Smartproxy – a proxy server provider that assists businesses in their data aggregation tasks. For now, let’s focus on the basics of data parsing and the challenges it presents.
How do data parsers work?
While we glossed over the basics of the process of data parsing, let’s talk about the functionality of the software that gets the work done. While simplifying the entire program by calling it a data parser, it consists of two parts – the parser and the lexer.
Parsing starts with the lexer inspecting the extracted code and segmenting it into separate tokens. It performs lexical analysis that scans the program one character at a time and organizes them into strings with a determined meaning.
When characters are organized into defined token values, structured information moves into the next stage – syntax analysis performed by the parser. It allows us to use organized tokens to construct them into parsing trees that order information in nodes based on their priority. The result should be a correct representation of information from a target website.
Of course, for fast search of information, the human brain still reigns supreme because it simultaneously performs extraction and storage of data because there is no need for parsing. But when we deal with large and continuous streams of information, we can automate a massive part of the process by utilizing these tools.
Data parsing challenges
Web scraping is an attractive first step of data extraction due to its simple automation. Data parsing slows down the process because it is very unpredictable. Even if you have a good parser that can collect information from multiple targets, you cannot predict the structure of other web pages, as well as updates in targeted websites.
Companies dedicate a surprising number of resources to data parsing for these exact reasons. While applying changes to parsers is not a difficult task, often performed by inexperienced programmers, the lack of automation opportunities requires a lot of involvement from the company personnel.
Different methods of web development force businesses to use multiple parsers to extract valuable data. Even single web pages can have different layouts for their online shops and other page types that may respond differently to parsing.
Because retailers and other E-commerce platforms are the most common targets of data extraction, constant changes in website structure will keep stopping parsers in their tracks.
While we may see automation possibilities in the future, the ever-changing nature of web pages and their development practices takes away from us the ability to create a parser suitable for every target. If you are interested in a career in data analytics, prepare yourself for monotonous work with data parsers.
Building a data parser vs buying one
With so many modern businesses relying on data aggregation, parsing is a real head-scratcher. Because it is the most resource-intensive process of information extraction, some companies may opt out of building their own data parser and choose to outsource these tasks. Let’s talk about the factors that can influence this decision.
If a company primarily uses data extraction to collect data from competitor retailers, it should make proper investments into data analytics and developer teams that could build and maintain their own parsers. It provides more accessibility and easier access to customization, helping you implement changes and continue aggregating data faster. However, sustaining your parsers requires a lot of maintenance and additional web servers to maintain the process.
Some businesses are less dependent on the collected information, and their need for essential data might come from different sources: social media platforms, online forums, and other targets with big differences between their website structure. In this case, it is better to buy parsing services from reliable partners to avoid constant adaptation and resource allocation for the maintenance of multiple parsers. When main company tasks rely less on the aggregated data, it is better to leave the delicate matter to professionals.
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