You have probably heard of web scraping and the different legitimate use cases for web scraping. Data parsing plays an important role in web scraping by converting one data format from one to another. It is commonly used for data structuring to make preexisting, often unstructured, and incomprehensible data more readable. Without data parsing, turning the data you scrape into useful solutions would be difficult.
Let’s go over what data parsing is in web scraping and whether developing an in-house data parser is more advantageous to a business or whether it’s preferable to buy a data extraction solution that already handles the parsing for you.
Data parsing – put simply
Data parsing is the process of transforming one type of data into another. This is a key process in turning your alternative data into insights that your business can learn from to boost commerce. If you receive your data in raw HTML, a parser will turn it into a more readable and understandable data format.
To put it into greater detail, the first step in programming is known as lexical analysis, and it involves breaking down a string into symbols. The second stage is called syntactic analysis, and it involves using those symbols to generate a parse tree that demonstrates how they are related to one another.
Reliable data parsers aren’t limited to a single format. You should be able to input any form of data and produce a different type of data. This could imply converting raw HTML into a format such as JSON, or taking data scraped from JavaScript-produced pages and converting it into a thorough CSV (Comma-Separated Values) file.
Data parsing for web scraping
You want to scrape a website, send a GET request, and store the HTML source on your device. This sounds relatively easy, however, not everyone would gain much value from downloading an entire webpage if they only require pricing information from a product page. In that scenario, it may be easier to copy and paste the information manually.
An issue arises whenever you download HTML pages and discover very few of them are well-formatted and legitimately readable. Unparsed data is full of symbols, and jumbles of upper and lowercase letters, all with virtually no spacing.
Web scraping with a proxy server paired with data parsing makes the process much more streamlined. A webpage that has been parsed is significantly easier to interpret. It not only excludes extraneous data, but it also cleanly organizes the information that is required.
The challenges of data parsing
Although it seems as though data parsing would make your web scraping efforts that much lighter, it has the potential to go amiss.
Inconsistent formatting is one issue you could face. The information you want to extract may be formatted differently on different pages. You may need to create unique parsing logic to detect and unify it. Another issue that could come up is frequently changing page structure. Large websites, particularly e-commerce sites, frequently update their HTML. Your parser will malfunction as a result, and you will need to change it.
If you are already utilizing residential proxies, your business has a competitive edge when it comes to web scraping. Once you overcome the challenges of data parsing, your web scraping will be even more useful to your business.
Building your parser versus outsourcing
Generally, building your tool is usually less expensive than purchasing software. However, many other factors should be considered before deciding whether to build or buy.
There are several advantages to developing your own data parser. For example, your data parser can be whatever you want it to be. It can be tailored to accomplish any task you desire, and developing your own parser is usually less expensive. Furthermore, you have complete control over any decisions that must be taken during the update and management of your parser.
Building your own data parser has upsides, but it requires a significant investment of your time and finances, more so if you need to create a sophisticated parser for parsing enormous amounts of data. More maintenance and human resources will be required because developing one will necessitate a highly competent development team.
Outsourcing a data parser may be the better option as you will not need to spend money on human resources. Everything will be handled for you, including the upkeep of the parser and servers. Any problems that develop will be resolved much more quickly as the individuals who develop the data parsing tools have considerable knowledge and are familiar with their technology.
Since a parser for the market needs to be unkept for various clients, it is also less likely that the parser will crash or have other problems since it will be tested regularly.
Conclusion
Data parsing is an essential process if you want to scrape data from the web. Without data parsing, it would be difficult to comprehend and pull the desired information from large datasets. By parsing data, you can take information and structure it in a way that is neatly organized and readable for most individuals to understand. Once your scraped data is easy to understand, you can put it to use to achieve your business goals effectively.
