What is Data Parsing?

Data parsing is the process of taking data in one format and converting it to another format. Parsers are used everywhere. They are often used in compilers when we need to parse computer code and generate machine code.

This happens constantly when developers write code that will run on hardware. Parsers are also present in SQL engines. SQL engines parse an SQL query, execute it, and return results.

A good data parser is not limited to specific formats. You should be able to input any type of data and output another type of data. This could mean converting raw HTML into a JSON object or retrieving data from JavaScript-rendered pages and converting it into a complete CSV file.

Parsers are widely used in web scraping because the HTML we receive is not easy to parse. It is necessary to convert the data into a human-readable format. This may involve generating reports from HTML strings or creating tables to display the most important information.

Although there are many use cases for parsers, this article will primarily focus on data parsing for web scraping, as thousands of people encounter this activity on the Internet every day.

How to Build a Data Parser

Regardless of the type of data parser you choose, a good parser will determine which information from the HTML string is useful based on predefined rules. Typically, the parsing process consists of two stages: lexical analysis and syntactic analysis.

Lexical analysis is the first stage of data parsing. It mainly creates tokens from a sequence of characters that come into the parser as a string of unstructured data, such as HTML. The parser creates tokens using lexical units like keywords and delimiters. It also ignores irrelevant information, such as whitespace and comments.

After the parser has separated the data into tokens and irrelevant information, it discards all irrelevant information and passes the relevant information to the next stage.

The next part of the data parsing process is syntactic analysis. This involves building a parse tree. The syntactic parser takes the relevant tokens obtained during lexical analysis and arranges them into a tree. All other irrelevant tokens, such as semicolons and curly braces, are added to the nested structure of the tree.

Once the tree is parsed, the information remains in a structured form that can be saved in any file type. There are several different ways to build a data parser: from building it programmatically to using existing tools. It all depends on your business needs, time, budget, and a number of other factors.

To begin, let's look at HTML parsing libraries.

HTML Parsing Libraries

HTML parsing libraries are great for automating the web scraping process. Many of these libraries can be integrated into your web scraper via API calls to parse data as it is retrieved.

Here are a few popular HTML parsing libraries:

Scrapy or BeautifulSoup

These libraries are written in Python. BeautifulSoup is a Python library for extracting data from HTML and XML files. Scrapy is a data parser that can also be used for web scraping. When it comes to web scraping with Python, there are many options, and it all depends on how practical you want to be.

Cheerio

If you are used to working with JavaScript, Cheerio is a great option. It parses markup and provides an API to work with the resulting data structure. Puppeteer can also be used. With it, you can generate screenshots and PDFs of specific pages, which can be saved and further analyzed with other tools. There are many other JavaScript-based web scrapers and parsers.

JSoup

For those primarily working with Java, there are options as well. One such option is JSoup. It allows you to work with real HTML using its API to fetch URLs, extract and manipulate data. It acts as a web scraper. Finding other open-source Java options may be challenging, but they are definitely worth looking into.

Nokogiri

There is an option for Ruby as well. Take a look at Nokogiri. It allows you to work with HTML and HTML in Ruby. It has an API similar to packages for other languages and allows you to query data obtained from web scraping. It adds an extra layer of security by treating all documents as untrusted by default. Data parsing in Ruby can be tricky, as it can be harder to find gems that work.

Regular Expressions

Now that you have an idea of which libraries are available for data parsing, let's discuss a common issue in HTML parsing: regular expressions. Sometimes the data inside an HTML tag is not well-formatted, and we need to use regular expressions to extract the desired data.

You can build regular expressions to get exactly what you need from complex data. Tools like regex101 make it easy to verify that the data is correctly selected. For example, you want to extract data from all paragraph tags on a web page. The regular expression might look like this:

/<p>(.*)<\/p>/

The syntax of regular expressions varies somewhat depending on the programming language you are using. In most cases, if you are using one of the libraries listed above or something similar, you won't need to worry about generating regular expressions.

If you prefer not to use any of these libraries, you might consider building your own parser. This can be challenging, but it may be worth the effort if you are working with very complex data structures.

Building Your Own Parser

If full control over the data parsing process is required, then building your own parser can be a very effective solution. Here are a few things to consider before building your own parser.

A custom parser can be written in any programming language. You can make it compatible with other tools you use, such as a web scraper, without worrying about integration issues.
In some cases, building your own tool can be cost-effective. If you already have a development team, this might not be too difficult for them.
You have full control over everything. If you want to target specific tags or keywords, you can do so. With any strategic update, you will have no trouble updating the data parser.

However, on the other hand, building your own parser comes with some challenges.

The HTML of pages changes constantly. This can become a problem for developers. If you do not anticipate that your parsing tool will be hugely important to your business, then taking time away from product development may be inefficient.
Building and maintaining your own data parser can be expensive. If you don't have a development team, you can contract the work, but that may lead to incremental bills based on developers' hourly rates. Additionally, you will have to pay for new developers to get up to speed.
Moreover, you need to purchase, build, and maintain a server to host the custom parser. It must be fast enough to handle all the data you send through it, otherwise you may encounter issues with consistent data parsing. You also need to ensure server security, as it may process sensitive data.

Having this level of control can be beneficial if data parsing is a critical part of your business; otherwise, it may add unnecessary complexity. There are many reasons you might need a custom parser, but make sure it justifies the investment.

Parsing schema.org Metadata

There is another way to parse web data: through site schema. Web schema standards are governed by schema.org, a community that promotes structured data schemas on the Internet. Web schemas are used to help search engines understand the information on web pages and deliver better results.

There are many practical reasons people want to parse schema metadata. For example, companies may want to analyze the schema for an e-commerce product to find updated prices or descriptions. Journalists might parse certain web pages to obtain information for their news articles. There are also sites that can aggregate data such as recipes, instruction manuals, and technical articles.

Schemas come in different formats. You may hear about JSON-LD, RDFa, and Microdata schemas. These are the formats you are most likely to parse.

JSON-LD stands for JavaScript Object Notation for Linked Data. It consists of multidimensional arrays. It is implemented using schema.org standards from an SEO perspective. JSON-LD is generally simpler to implement because the markup can be inserted directly into an HTML document.
RDFa (Resource Description Framework in Attributes) is recommended by the World Wide Web Consortium (W3C). It is used to embed RDF statements in XML and HTML. A significant difference from other schema types is that RDFa only defines a meta-syntax for semantic tags.
Microdata is a WHATWG HTML specification used to embed metadata into existing web page content. Microdata standards allow developers to create custom vocabularies or use others, such as schema.org.

All these schema types can be easily parsed with various tools in different languages. There is a library from ScrapingHub, another from RDFLib.

Existing Data Parsing Tools

We have discussed a number of existing tools, but there are other great services as well. For example, the Google Search API. This tool allows you to collect search results in real time without worrying about server uptime or code maintenance. To start collecting and analyzing web data, you only need an API key and a search query.

There are many other tools for analyzing web data, such as JSoup, Puppeteer, Cheerio, or BeautifulSoup.

The advantages of purchasing a web scraper are as follows:

Using an existing tool requires little maintenance effort.
You will not spend a lot of time on development and configuration.
You get access to a support team specifically trained to use