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What is Data Parsing?

Data parsing is the process of converting data from one format to another. Usually, it turns scrambled and hard to read strings into easier to understand formats such as plain text, CSV, JSON, and others.

There are many different use cases for data parsing, mostly involving various analysis methods. Most often, it is performed during web scraping or other automated data collection processes as these will deliver unreadable scrambles, behind which lie valuable information.


How does a data parser work?


Data parsers will often take any type of input and attempt to use various rules to reformat text. Usually, data parsing happens over several stages. These can be defined as:


  1. Loading input. Data parsers will accept strings and files of various formats. Most frequently, raw HTML files will be the starting point. These are difficult to analyze as they store valuable information that’s interspersed with lots of irrelevant (for analysis) code.
  2. Defining value. In almost all cases, only a small part of a raw HTML or other file will be valuable. For example, in web scraping, ecommerce data such as product pricing will often be considered important while layout information might be less so.
  3. Separating irrelevant data. Data parsers will scrub out and discard all irrelevant data, some of which is nearly always removed such as whitespaces or developer comments.
  4. Building a parse tree. Parsers almost always move unstructured data into a structured format. Such a data format usually has some form of hierarchy (e.g. JSON), therefore building a parse tree is necessary.
  5. Creating tokens (also called lexical analysis). Data parsers turn sequences of characters into tokens, which represent lexical units (i.e. words, chains or parts of words).
  6. Moving relevant tokens to the tree. As the parsing process nears completion, all the remaining data is sorted into branches. After the data is prepared, it can be exported into a file format.


Some of these steps happen (nearly) simultaneously. They are intended more to be understood through an analytical lens of the data parsing process.

While the structure of data parsing technologies are more or less similar, the way it's accomplished differs. Two primary methods can be outlined for converting data - grammar-driven and data-driven.

The two types of data parsing


Grammar-driven data parsing


As the name might suggest, the method revolves around the formal set of grammar rules. The goal of such data parsers is to uncover sentences within the unstructured data such as a HTML string. These are then turned into a structured format.


Grammar-driven data parsing tools struggle in several regards. For one, not all texts behave in perfect unison with grammar rules. These exceptions may be especially grave in web data where the rules might be a little more relaxed.


As a result, some rules will have to be relaxed in the data parser, which can lead to more issues within more strict structures. Some semantic analysis might be required to reorganize the structured format in order to produce the desired result, especially since some sentences might be ambiguous without assigning meaning to specific words.


Finally, many languages will have different grammar rules, which means these data parsers will not be transferable. Building a completely new one will be required to parse data for different languages.


Data-driven data parsing


On the other hand, data-driven parsing includes a multitude of methods. The core of such a data parser is based on a probabilistic rather than a deterministic model. In practice, however, several rule-based approaches might be used, some of which may include syntactic analysis and a semantic analysis component.

Additionally, advancements in natural language processing (NLP) will often be incorporated into such a data parser, making it significantly more flexible. Finally, statistical methods can overlap with several languages, allowing for better interlinguistic coverage. Data-driven data parsing can even work on conversational content, which can be nigh impossible for the other method.


Use cases and benefits of data parsing


Data parsing is heavily used in many business applications. Wherever data extraction happens, parsing is likely to follow. These connections become even more important whenever scraping web pages is involved.


Web pages are intended to be viewed in browsers and not be analyzed with tools. As such, data extraction processes deliver a jumbled mess that’s nearly impossible to interpret for humans, making parsing a necessity.


A data parser, for example, will be employed in investment analysis, if it involves numerous sources. Whenever multiple data extraction sources are involved, the structuring methods will differ, necessitating the usage of a data parser. If web scraping tools are involved, the usage of a data parser becomes unavoidable.

Another common use case for data parsers is to support market research. Collecting online data through the usage of scraping has become a popular method for measuring trends in markets. Since, however, an inordinate amount of data has to be collected from dozens of different sources, parsers are a necessity.


How to build your own data parser


We will bypass any data gathering considerations and assume the necessary tools are already in place. Note that building your own data parser is quite complicated and can take a considerable amount of time.


  1. Decide on the programming language. Nearly any programming language can help you build your own parser as long as it supports something like RegEx.
  2. Get a sample document. Before you get started with data parsing, you’ll need something to test your code on. Get a jumbled data format that you’ll use for parsing.
  3. Create rules. Whenever you build your own parser, creating rules (even if you don’t use a grammar-driven approach) will be necessary as you’ll only be interested in a part of the data.
  4. Decide on an output format. Data parsing doesn’t do much if it doesn’t create a new file with a significantly better structure than before. Popular data parsing formats include JSON, CSV, XSLX.
  5. Check with an interactive data language. Data parsing is primarily used for analysis purposes, however, few people do so manually. Use your own parser to see if the output is viable for other applications and languages.

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By Oliver Jones
Oliver is someone you would call a tech-wizard. Fascinated with everything computer and machine related, he has been involved in the industry for ages. Proxies and data are his two newest interests that have carried him to the field of writing. Oliver believes that all the knowledge in the world is worth nothing if it can’t be shared!