Businesses today rely a lot on data to make decisions. Businesses can use this data for market research, pricing intelligence, identifying trends, keeping track of competitors and much more. However, collecting this data is where the challenge comes in. If you’ve been looking at collecting more data for your business, you’ve probably come across the terms web scraping and data parsing? But what does this mean, and how do these processes
work?
Once you start collecting data online by using a scraper, it won’t be in a format that is easily understandable. That’s because the data collected will be in HTML code snippets, as this is the format that websites use to understand what they need to display. This raw data has to go through a few more steps before becoming usable, which is where data parsing comes into the picture.
In this article, we’ll look at what data parsing is, how it’s used and the different types. We’ll also be looking at some of the challenges of data parsing, such as parsing errors, maintenance and more.
What Is Data Parsing?
Simply put, data parsing takes the collected raw data in one format and transforms it into a different format. A traditional parser would take the HTML code that’s been collected and convert it into readable text. However, there are other ways that parsers can convert data depending on the user’s needs.
A data parser does not collect the data. That is a different and unrelated process. Nor does it understand the content of the data that’s been collected, so it cannot be used to analyze the data – that’s another process yet again. However, the parser is still a critical component and one of the most important processes because without it, you’ll be stuck with heaps of code snippets that you can’t make heads or tails of.
Data parsing is not a complex process, and building your own is relatively easy if you have some programming experience. However, despite its simplicity, there are still a few challenges such as parsing errors, maintaining the parser and making frequent updates that can cause headaches for anyone working with these tools.
Types of Data Parsing
There are two main types of parsing techniques when it comes to data parsing.
Top-Down Data Parsing
This type of parsing first looks at the highest level of the parse tree and then works its way down. The top-down parsing approach focuses on breaking down a big problem into smaller chunks that are easier to understand.
Bottom-up Data Parsing
This type of parsing looks at the lowest level of the parse tree and works its way up and from right to left. This approach focuses on solving the smaller problems at a basic level and then integrating them into the whole solution.
Choosing the right data parsing technique—top-down or bottom-up—depends on the nature of your data, the complexity of your parsing needs, and the specific requirements of your application. For simpler grammar structures or when early error detection is crucial, top-down parsing is generally preferable.
It’s easier to implement for straightforward grammar and is efficient at predicting and parsing expected structures. However, it may struggle with complex or left-recursive grammar and can be more memory-intensive due to its recursive nature.
On the other hand, bottom-up parsing excels with complex and ambiguous grammar. It’s more adaptable, capable of handling a wide range of grammar without specific adjustments, and tends to be more efficient for parsing large datasets. Though more complex to implement and typically identify errors later in the process, bottom-up parsers are robust and flexible for general-purpose parsing.
In some cases, experimenting with both approaches may help determine the most effective balance of performance, ease
What Is Data Parsing Used For?
Although data parsing is most commonly associated with web scraping, the truth is that it’s used much more than is realized. When you open up a website and read a post, a data parser is responsible for converting the HTML and code into text that you can read. The same goes for games, apps, web extensions and more. It is safe to assume that any processes you use online use a data parser in some way.
With backconnect proxies, data parsing becomes even more powerful and versatile. Backconnect proxies, which rotate IP addresses automatically, enable parsers to access data from websites without being blocked or banned. This is crucial for tasks that require gathering large amounts of data from various sources across the internet, such as market research, SEO optimization, competitive analysis, and more.
Data Parsing Challenges
As with any form of technology, there are a few challenges to be aware of when using a data parser. For one, parsing errors can really throw a spanner in the works. These parsing errors happen when there are mistakes in the syntax or code of your parsing program. This commonly occurs when you or your IT team build your own parser. Another challenge with data parsing software is maintenance. Parsing software requires frequent maintenance and updates to continue working. When you’ve built your own parser, it will take a lot of time and resources to maintain and update the program. While this isn’t complicated and is often a task that Junior Programmers or Developers can easily do, it’s not a task that really improves their skills and can become an annoyance to the programmer and managers.
In these cases, it may become worthwhile to consider paying a subscription for a parsing program. These may seem expensive, and they do limit the customization of the program and uses, but they come with updates, support and maintenance, which leaves your IT team free to focus on other tasks.
Final Thoughts
Data parsing is used all around us and not just in web scraping. Therefore it is important to understand what parsers are and how they work. With this knowledge, it is easier to understand the challenges such as parsing errors and frequent maintenance and how to overcome them.
