Skip to main content

What is Data Parsing? In Detail!

· 10 min read
Oleg Kulyk

Hard Data Vs. Soft Data Explained!

Data equals power, and there is no denying that. In today's world, it is all about data as people's lives are becoming more virtual. But what good is this resource if it is not handled and formatted correctly? This is where data parsing comes into play. So sit back and get ready to go through Data Parsing 101 as we dive headfirst into learning what data parsing is all about.

Let's begin!

What is Data Parsing?

Simply put, Data Parsing is a method of converting data to human understandable data. Generally, most of the data available for extraction online exists in a machine-readable format, but humans cannot understand it directly. This is where data parsing comes in. Data parsing allows users to convert the collected data into text that is readable by humans.

However, data parsers cannot extract data like scrapers and crawlers. It cannot even understand the data that is collected, and it also cannot read the data that is converted. For this reason, it is used together in conjunction with data collecting bots and AIs called scrapers and crawlers to be of use.

Now let's take a look at the different approaches for parsing data:

What Are The Types of Data Parsing?

Data parsing is of two types depending on the approach used to parse the data. These are:

Manual Data Parsing

Manual data parsing is the process of manually converting data into a readable format. This is done by a human being who is skilled in data parsing. This is the most common method of data parsing and is used by most companies. However, it is also the most time-consuming and expensive method of data parsing.

Automated Data Parsing

Automated data parsing is the process of converting data into a readable format using a computer program. This is done by a computer program that is skilled in data parsing. This is the most efficient and cost-effective method of data parsing. However, it is also the most difficult method of data parsing.

Based on the parsing mechanism, there are also two approaches of data parsing:

Data-Driven Approach

his approach of Data Parsing analyzes and structures the result into human-readable text using various methods, including Natural Language Processing; NLP for short. It also utilizes semantic equations and intelligent statistical parsers. This allows it to convert languages precisely, even in a conversational tone.

It also skips the deductive approach used by Grammer-driven Data Parsing Methods and uses modern treebanks.

Grammar Driven Approach

As the name suggests, this method of data parsing is focused on grammar. It utilizes proper grammatical rules and regulations for parsing the collected data. It analyzes data using a deductive approach and structures sentences from raw, uncategorized, and fragmented data.

The grammar-driven approach is somewhat bland, meaning it's often missing the variety and robustness the data-driven approach offers.

However, there is a workaround for that, as you can simply do it by being flexible with the grammar constraints.

How Do Data Parsers Work?

The working procedure of Data parsers is relatively simple:

  • First of all, the user decides how the data will be parsed as per requirement, sets the rules, and writes the code.
  • The data to be parsed is then collected by the user.
  • That raw and unstructured data is fed to the parser.
  • The parser then sorts and analyzes the data.
  • Finally, it parses the data according to the code and rules set by the user and outputs it accordingly.

What Are The Advantages of Using Data Parsing?

The advantages of using data parsers are as follows:

It Converts Data Properly

Data parsers serve as a translator between users and machines as they translate machine-readable data and information to human-readable text and sentences, It does so by breaking up the fragmented and unstructured data into small pieces and then analyzing them separately. This results in much more structured data and always matches the user's desired output structure.

It Saves Time by Optimizing

Data parsers save time by eliminating the need for manual inputs and interactions from the user by automating its working process. Data parsers always use the most efficient algorithms to convert and translate data. They also simplify massive amounts of data and make them easy to handle and read. This results in a much more efficient and optimized method that very few other software and applications can offer.

It Saves Money

Data parsers save money by eliminating the need for human labor and resources. Data parsers are automated and do not require any human interaction or labor. This results in a much more cost-effective method of data parsing.

It Is Flexible

Data parsers are very flexible and can be used for a variety of purposes. They can be used for data parsing, data mining, data analysis, data visualization, and much more. This makes them very versatile and useful for a variety of purposes.

What Are Some Practical Uses of Data Parsing?


Since parsers are able to handle large amounts of data, they come in handy when dealing with accounting data. Banking companies often extract and collect vast amounts of data for customer insights, their credit scores, their financial records, etc., and so data scrapers help to analyze this information and data efficiently.


Emails have become the quintessential means of communication in today's world, and companies receive and exchange massive amounts of emails on a daily basis. Dara parsers are excellent at handling emails. Parsers can extract information and categorize it according to any setting the user sets. They are able to condense and format the data and also able to summarize and categorize emails more efficiently and effectively. This saves a lot of time humans would require manually handling them.

Social Media

Social media is a huge platform for businesses to advertise and market their products and services. Data parsers are able to extract and analyze data from social media platforms such as Facebook, Twitter, Instagram, etc. They are able to extract data from posts, comments, and even from the user's profile. This allows companies to analyze and understand their customers better and also helps them to market their products and services more effectively.


The use of data parsing is crucial in eCommerce; you can use data parsing for:

  • Product analysis
  • Market analysis
  • Customer feedback and reviews
  • Marketing
  • Customer behaviour analysis
  • Product placement and pricing
  • Product recommendations


Human resources departments can use data parsing to make their lives easier. For instance, when selecting interview candidates, they can quickly and efficiently use data parsing to blitz through the hundreds and thousands of resumes, cover letters, applications, etc. This helps them narrow down on the applicants easily and also expedite and helps their decision-making.

Besides these, there are countless other uses for Data Parsing, which also include:

  • Data scraping and crawling.
  • Languages such as Java, Python, HTML, etc.
  • In conjunction with databases
  • In conjunction with data visualization

Building vs Buying a Data Parser

Whether you should build or buy a data parser depends on a wide array of reasons as they have pros and cons.

Why Should You Build a Data Parser?

You should build a data parser because:

  • In most scenarios, building a parser is usually the cheaper option. That being said, only attempt to build a parser in-house if and only if you have a dedicated IT department. Like any other software or application, parsers are also subject to things like regular maintenance, bug fixes, glitches, updates, etc. So having a dedicated IT team is crucial.
  • It is customizable. Of course, any custom-made software is going to be customizable. You can have a parser made which is specifically tailored to your preferences and needs.
  • You get to decide how things are done. For instance, you get to choose the programming language in which the parser will be written and set its compatibility with other software and bots. As a result, your end product will be one that can be integrated with other elements easily and conveniently to serve your needs.

What Things To Consider When Building a Data Parser?

You should consider the following things when you decide to build a parser:

  • First and foremost, you must have a dedicated IT team. That same team will be responsible for everything related to the parser, from building it to its maintenance. So it is essential to have an in-house IT team.
  • It is going to take a long time to build and develop.
  • You are likely to run into problems like crashing and malfunctioning.
  • You'll need to spend a lot of money, for instance:
    • To buy a server
    • Pay for its maintenance
    • Pay salaries to an entire team.

Why Should You Buy a Data Parser?

You should buy a data parser because:

  • You don't have an IT team. This is probably the best decision you can make since you don't have an IT team. Not having a dedicated in-house IT team means you'll have to fix everything by yourselves. This can be unproductive and time-consuming. So just buying a data parser becomes the easier and more reliable option.
  • You don't want to spend on extra personnel annually. For instance, when you face any technical problem, your problems will be solved quickly, or you will at least be offered a solution as fast as possible.
  • They are more reliable. The fact that those parsers are available for sale means other people have also used them. So it is likely that it won't have too many bugs and glitches. Since they have fewer problems and are already being used by others, they are more likely to perform just as well for you. You can easily ensure that they are reliable by simply checking customer reviews.

What Things To Consider When Buying a Parser?

You should consider the following when you decide to buy a parser:

  • Customer reviews! Yes, this is the first thing you need to notice and evaluate when deciding to purchase a parser. Use customer reviews to help you narrow down your choices and options.
  • It is going to be expensive.
  • It may require training, so consider the learning curve when migrating to new software.


I hope this article has helped you develop a better understanding of data parsing. So now that you know more about it, I hope you'll be able to put this information to good use. If you wish to learn about an interesting event related to data then visit this link here.

Happy web scraping and don't forget to update your data parsing CSS selectors 📝

Forget about getting blocked while scraping the Web

Try out ScrapingAnt Web Scraping API with thousands of proxy servers and an entire headless Chrome cluster