Data Parsing: What’s Involved and What Do You Need to Know?
As you dive deeper into the world of proxies and web scraping, it’s natural that you’d want to learn more about how to make sense of the information you’re collecting — and learning to differentiate technicalities like the differences between web scraping and data parsing.
While terms like web scraping and data parsing are often used interchangeably, the technologies require two different skillsets and knowledge bases.
That’s why we’re going into more detail about the differences between data parsing vs. web scraping. By doing so, you’ll gain a better understanding of concepts tied to data parsing. You’ll also have a better sense of how to gain more insights from the information your organization collects via web scraping. If you’re looking for specific information, use the table of contents to jump to that section.
What is Data Parsing?
One of the simplest ways to define parsing data is that it takes information accumulated from various data collection methods, like web scraping, and transforms it into a readable format. The concept of parsing may be familiar to those who understand how computers have compilers that take computer code and turn it into machine code.
Data parsing comes after organizations have run their scraping robots to extract information from web pages. The next step involves changing that information into a helpful format for analysts and other business stakeholders. Data parsing analyzes a string containing symbols for a specific language and turns it into a more structured format.
Why Is Data Parsing Important?
When you use a web scraping tool, the content typically comes out in a raw HTML format. That’s not something most people or programs can understand at first glance. Data parsing is important because that’s how we take that HTML code and convert it into information stored in a database table, JSON file, or CVS format.
An analyst needing to create a report for company shareholders won’t be able to do much with your web scraping data when it’s first pulled down from the internet. Parsers are the key to making information available across enterprises.
Let’s say your company is heavily invested in AI and machine language technology. Data parsing is essential in building reusable processes that are capable of learning from the continuous information collected via web scraping.
One reason parsers are so essential to web scraping is that you rarely collect information that you can make sense of at first glance. You need a way to change that raw code into something capable of being read by a person or even an ML process.
How Does Parsing Data Work?
Parsing data starts with analyzing the data collected from web scraping and other information collection efforts and rearranges it into syntactic components. Next, the parser decomposes and transforms the information into formats capable of processing via other formats. Let’s take a deeper look at how that happens.
Lexical analysis involves converting a sequence of characters pulled from web scraping and arranging them into strings that have meaning. Each of these strings represents a token. Next, a program called a lexical analyzer, or lexer, works to turn that string into a structured data format. During this stage, the parser removes any irrelevant information and moves anything useful to the next step in the parsing process. All this happens before the execution of the parser.
Syntactic analysis involves the actual execution of the parser. First, all data gets pushed through the parsing component based on the logic written into the software code. At this point, data gets checked against various syntax checks to find meaning based on standard rules of grammar. Then, any relevant tokens get arranged into a tree format. Finally, information not considered valuable, like semicolons or commas, is added as part of the tree’s nesting structure.
How to Parse Data: Techniques
Data parsing techniques help find the basic connection between a string of characters arranged in our tree structure and basic grammar rules. That’s how we make sense of what may look like a random arrangement of information.
Top-down data parsing involves starting your search at the top of our parsing tree. From there, the parser works its way down the structure using the rules of grammar baked into the parsing code. You’re looking for derivations to the left of the input string.
The decisions made during top-down parsing are driven by figuring out which rule to apply to arrange the input string, or token, in the correct order. The process continues until you’re at the end of the parsing tree.
Top-down parsing involves breaking your token into parts to identify what needs to happen. From there, the program gradually works to make the string less complex and easier to read. One advantage of the top-down data parsing process is that you can build components in one solution that you can use for other purposes.
With bottom-up parsing, you’re starting at the lowest level of your parse tree. From there, you work your way up through the token while applying grammar rules. The goal is to work your way back to the start of the parse tree. The bottom-up parsing technique uses the right-most derivation.
Instead of breaking a problem into smaller pieces, bottom-up parsing solves the smaller issues first. It then pulls them into a whole to form a complete solution. The modules used in a bottom-up parser tend to communicate more with each other. Because you’re relying on concepts like data encapsulation (hiding the inner workings of a piece of code), you end up with less redundant code in your solution.
Common Technologies Using Data Parsing
A flexible data parser can work with a variety of technologies used to make sense of information. For example, you can use a scripting language like C# or Perl to build commands that let you execute a parsing program without compiling code.
Web scraping tools often bring back information in the original HTML format. However, you may also end up working with data housed in XML, a format used to transfer data between web applications. In addition, interactive data languages used for data analysis, manipulation, and visualizations also rely on data parsers.
Other technologies often used with data parsers include:
- SQL (Structured Query Language)
- Modeling languages like UML
- Internet protocols like HTTPS
How Do You Implement a Data Parser?
When considering the kind of data parser you need, the first thing you have to do is consider the grammar rules that should apply to your data. Then you can build a parser from scratch or locate an HTML parsing library that you can add to your web scraping tool. That way, you can send API calls that start parsing your data the moment you bring it down.
Regular Expressions and Data Parsing
It’s also a good idea to learn more about regular expressions to aid with data parsing. A regular expression, or regex, is a string of text used to find patterns within text.
Most programming languages allow you to apply a regex for parsing purposes. One of the great things about using regex is that they can save you a lot of time and effort when you’re parsing a lot of information.
Keep in mind that regex syntax varies from one language to another. However, there are similarities between all of them. Once you learn the regex patterns of one program or language, it gets easier to read them when written for other formats. Once you master regex, you can use them to extract information from nested fields in your parsing trees.
Building a Data Parsing Infrastructure
If you’re not interested in using a parsing library, you always have the option of building a custom parser. While that’s more challenging, it can be worth it if you’re dealing with complex data that present a unique challenge not addressed by existing parsing libraries.
Here are some things to keep in mind as you go about constructing your parser:
- Make sure the language you use is compatible with that used with your web scraping tool. That way, you won’t run into any integration problems.
- Account for the cost of building a new parser versus using a pre-existing one. Organizations that have a team of in-house developers may be able to accomplish the task quickly.
- Think about the amount of maintenance necessary to keep your parser updated based on changes made to HTML pages. It may not be an effective use of your developer’s time to have them constantly making updates to a custom parser. Instead, contracting the work out may be a more cost-effective option.
- You’ll have to construct a server capable of hosting your parser. In addition, the server must have enough power to allow for speeding along processing of the data collected through your web scraping efforts. Otherwise, you’re going to run into a lot of issues supporting consistent data parsing.
Handle Data Parsing and Web Scraping in One Shot
Scraping Robot removes many of the headaches involved in web scraping. Our solution helps you get around blocks, Captcha issues, and the need for browser scaling. In addition, the platform handles data parsing for you — so there’s no need for you to construct a custom solution.
Blazing SEO helps companies find suitable residential, and data center proxies for all your web scraping needs. Reach out to us today to learn more about how we can help you find the right tools for your company.
The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.
Start a risk-free, money-back guarantee trial today and see the Blazing SEO
difference for yourself!