What is data parsing?

Data parsing is the method used to extract data from unstructured sources and convert it into a structured format. This makes it easier to analyze, send, and integrate.
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Related terms: Dataset | Web scraper | Python | HTML

What is data parsing?

Data parsing extracts data from unstructured sources and converts it into a structured format. This process makes it easier to analyze, transmit, and integrate data. Over 80% of generated data is unstructured, so we parse it to extract relevant data points and save them in formats like JSON, XML, or CSV. 

Unstructured data exists everywhere, from comments on social media to reviews on product pages, or even reports available in PDF formats. By converting them into structured formats, they become usable. Each data source has specific methods and tools for extracting data from them.

How does data parsing work?

Data parsing involves the following steps:

  1. Identify the data source and how to get it: You need to find out where you can get the required data. For example, if the data is available publicly on the web, you need web scraping tools to fetch it.
  2. Select the appropriate parser: This varies, depending on the data source. For example, a PDF data source requires you to use a PDF parser. Web pages will require HTML parsers. 
  3. Extract and validate parsed data: With the parser, you can identify the specific data points, extract them, and then validate and clean them. The goal here is to make them consistent with the data type you want while making sure you are extracting the right information.
  4. Transform data into structured format: After validation and cleaning, you can then convert the data into your desired format such as JSON, or XML, or save them in relational databases.

Transforming data

There are four ways to transform data. These include mapping data fields, data type conversion, data enrichment, and output generation.

  1. Mapping data fields: This involves assigning extracted data points to specific fields in the target format. An example of this is assigning name values as values to the name key in JSON.
  2. Data type conversion: Numbers in textual form are available as strings. With this, you are able to convert it to an integer or decimal value before saving it.
  3. Data enrichment: This is basically adding more context to the data point. For example, you might want to add a currency sign to a price tag.
  4. Output generation: With the data structured in the right way and format, you can go ahead with generating the output.

Common textual data parsing techniques

  1. Tokenization: This is the process of breaking text data into smaller units known as tokens. These could be simple words or phrases. The input is a string of characters and the output is a sequence of tokens with a token considered as a meaningful element. 
  2. Syntax analysis: Analyzing the structure of the data to ensure it follows predefined rules or grammar. This step identifies relationships between tokens and validates the data's structure. It builds a parse tree or abstract syntax tree (AST) that represents the hierarchical structure of the input.

What is data parsing used for?

Data parsing is a versatile process with a wide array of applications, including extracting information from online content and effectively managing data from various software interfaces and configuration files.

Extracting data from web pages

The term web scraping is used for the process of extracting data from web pages. At it core is data parsing through which the downloaded HTML page is parsed and relevant data extracted which is then converted into structured format for further processing or saving. Parsing in this sense also applies to extracting data from PDF and other document formats.

Processing API response

API responses structured as JSON are converted into strings before sending to clients. You need to parse it using JSON libraries back into JSON before you can access individual data points in a structured way. All JSON libraries have a method for this. In Javascript, it is known as ‘parse’.

For example, in Python, you might receive a JSON response as a string like this:

import json

json_string = '{"name": "Product A", "price": 25.99}'
data = json.loads(json_string)
print(data["name"])
print(data["price"])

Reading and writing configuration files

Configuration files in most cases, have the .json file extension as the data stored in them are in JSON format. However, the actual format is string. To access specific configuration settings, you need to load the file, convert it from string to JSON, and then access individual configuration fields.

Challenges of parsing data

Effectively parsing data is not always straightforward. Several hurdles can arise when attempting to extract meaningful information, ranging from inconsistencies in data format to performance considerations.

Handling irregular or malformed data

Data is not always available in the way you want it. And in some cases, they can be malformed. Take, for instance, a server might respond with a malformed JSON string where one of the curly braces has been omitted. Reaching out to them to fix this is the solution here as JSON parsers will throw a JSON decoding error.

Dealing with complex data structure

Simply JSON data are easy to handle but when you begin to deal with complex nested data points that are dynamically generated, it adds some level of complexity. The way to go about it is to develop accessing logic that is smart and can reach the data points you want. To avoid running into exceptions, you can look to make sure the key is present before you try to access it. Alternatively, closing early by raising an exception is also a good practice.

Performance consideration

Parsing operation in most cases is an O(n) operation. This basically means that as the size of the data to parse grows, so will the time taken to parse the data. While you can parse small JSON files in under a second, if you are given a JSON file with 500MB of data, it could take minutes to completely parse.

Common parsing tools and libraries

General parsing

You can extract data from text by using regular expression popularly known as regex. This is helpful for extracting emails and phone numbers from web pages and documents. However, regex is not just useful in contact parsing, it is specifically meant for parsing sections of text that match specific patterns.

JSON parsing

JSON parsing is done using the JSON library. There is a library for parsing and encoding JSON data in most programming languages. For Python, the built-in JSON module is the library for that. It comes with two methods (load and loads) for that. The load method parses JSON from files while loads parse JSON from text. Javascript also has its own method for parsing known as JSON.parse. 

XML parsing

Parsing data from XML documents is also easy as there are multiple libraries provided in each popular programming language. For example, Python programmers can use the lxml library. NodeJS/Javascript programmers, on the other hand, use DomParser API, xml2js, and a few other available XML parsing libraries.

HTML parsing

Python programmers often use Beautifulsoup, which is a wrapper around the lxml and HTML.parser library. Scrapy and a few other scrapers also have their parsers embedded. For Javascript developers, the DomParser works just fine. However, Cheerio is a good alternative too.

Automate data parsing with SOAX scraper APIs

Parsing web data has its challenges ranging from human errors to how web pages frequently change, requiring you to update your parsing logic. SOAX scraper APIs automatically download and parse data for you. SOAX scrapers support over 20 ecommerce sites and 30 search engines.

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