What Is Data Parsing? Definition, Benefits, and Challenges

In this article, you will learn everything you need to know about data parsing. In detail, you will learn what data parsing is, why it is so important, and what is the best way to approach it.
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Ella Siman
Ella Siman | Product Marketing Manager
18-Jan-2023

Follow this blog post and become an expert on data parsing. Here, you will see:

What Is Data Parsing?

Data parsing is the process of transforming data from one format to another. In detail, data parsing is typically used for structuring data. This means converting unstructured data into structured or more structured data. So, data parsing is generally performed by a data parser to convert raw data into formats that are easier to analyze, use, or store.

Data parsing is done via APIs or libraries and is particularly useful when it comes to data analysis, data management, and data collection. You can use a data parser to break up a large data set into smaller pieces, extract specific data from a raw source, and convert data from one structure into another. For example, given an HTML page, a data parser programmed correctly will be able to convert the data contained in the document into a format that is easier to read and understand, such as CSV.

Data parsing is used daily in various industries, from finance to education, from Big Data to ecommerce. A well-made data parser extracts relevant information from raw data automatically, without any manual work. Then this parsed data is used for market research, price comparisons, and more.

Let’s now learn how a data parser works.

What Does a Data Parser Do?

A data parser is a tool that takes data in one format and returns it in another. Thus, a data parser receives data as input, elaborates it, and returns it in a new format as output. So, a data parsing process is based on data parsers, which can be written in several programming languages. Note that there are several libraries and APIs available for parsing data.

Let’s understand how a data parser works through an example. In detail, let’s assume you want to parse an HTML document. Then, the HTML parser will:

  • Receive an HTML document as input.
  • Read the document and save its HTML code as a string.
  • Parse the HTML data string to extract the information of interest.
  • Elaborate, process, or clean the data of interested while parsing, if required.
  • Convert the parsed data into a JSON, CSV, or YAML file or write it to a SQL or NoSQL database.

Note that the way the data parser parses data and converts it to a format changes based on how the parser is instructed or defined. Specifically, this depends on the rules passed as input parameters to a parsing API or program. Or in the case of a custom script, it depends on the way the data parser is coded. In both cases, no human interaction is required, and the parser will process the data automatically.

Let’s now see why data parsing is so important.

Benefits of Data Parsing

Parsing data comes with several benefits, applicable in many industries. Let’s take a look at the most important reasons why you should adopt data parsing.

Time and Money Saved

Data parsing allows you to automate repetitive tasks, saving you time and effort. Plus, transforming data into more readable formats means that your team will be able to understand the data faster and perform their tasks more easily.

Greater Data Flexibility

Once you parse data and convert it to a human-friendly format, you can reuse it for different purposes. In other words, data parsing increases the flexibility of your data processes.

Higher Quality Data

Typically, converting data to more structured formats requires cleaning and standardizing the data. This means that data parsing improves the overall quality of your data.

Simplified Data Integration

Data parsing encourages you to transform data from multiple sources to a single format. This helps you integrate different data into the same destination, which can be an application, algorithm, or process.

Improved Data Analysis

Dealing with structured data makes studying and analyzing data easier. This also leads to deeper and more accurate analysis.

Challenges in Data Parsing

Dealing with data is not easy, and data parsing is no different. The reason is that there are several obstacles that a data parser is called to face. Let’s see three challenges that you should keep in mind.

Handling Errors and Inconsistencies

The input to a data parsing process is typically raw, unstructured, or semi-structured data. As a result, the input data is likely to contain errors, inaccuracies, and inconsistencies. HTML documents are one of the most common cases where you can find such issues. This is because most modern browsers are smart enough to correctly render HTML pages even when they contain syntax errors. So, your input HTML pages may contain unclosed tags, invalid HTML content according to W3C (World Wide Web Consortium), or simply special HTML characters. To parse such data, you need an intelligent parsing system that can automatically address these problems.

Dealing With Large Amounts of Data

Parsing data takes time and system resources. Therefore, parsing can lead to performance issues, especially when Big Data is involved. For this reason, you might have to parallelize your data processes to parse several input documents simultaneously and save time. On the other hand, this would increase resource usage and the overall complexity accordingly. So, parsing large data is not an easy task, and it requires advanced tools.

Handling Different Data Formats

A powerful data parser must be able to handle several input and output data. This is because data formats evolve as rapidly as the entire IT industry. In other words, you need to keep your data parser up to date and able to handle different formats. Plus, a data parser must be able to import and export data in different character encodings. This way, you will be able to use the parsed data on both Windows and macOS.

Building vs. Buying a Data Parsing Tool

As it should now be clear, the effectiveness of a data parsing process depends on the parser used. Therefore, it comes naturally to ask whether it is better to let your technical team build a data parser or simply adopt an existing commercial solution, such as Bright Data.

Building your own parser is more flexible but more time-consuming, while buying it is immediate but gives you less control over it. Obviously, the matter is more complex than that. So, let’s try to figure out whether it is better for you to build or buy a data parser.

Building a data parser

In this scenario, your company has an internal development team that can build a custom data parser tool from scratch.

Pros

  • You can adapt it to your specific needs.
  • You own the data parser code and have control over its development process.
  • If used a lot, it may be cheaper in the long run than paying for a pre-built product.

Cons

  • The cost of development, software management, and server hosting cannot be ignored.
  • Your development team will have to spend a lot of time designing, developing, and maintaining it.
  • There might be some performance problems, especially if the budget for a powerful server is limited.

Building a parsing tool from scratch always has its benefits, especially if it needs to meet particularly complex or specific requirements. At the same time, this requires a lot of time and resources. So, you may not be able to afford it or simply want your highly-skilled team not to waste time on building such a tool.

Buying a data parser

In this case, you buy a commercial solution that offers the data parsing capabilities you are looking for. This typically involves paying a software license or a small fee per API call.

Pros

  • Your development team will not waste time and resources building it.
  • The cost is clear from the beginning and there are no surprises.
  • The provider will take care of upgrading and maintaining the tool, not your team.

Cons

  • The tool may not meet your future needs.
  • You do not have control over the tool.
  • You may end up spending more money than building it.

Buying a parsing tool is quick and easy. After a few clicks, you are ready to start parsing data. At the same time, if you choose a tool that is not advanced enough, it may fall short very quickly and not meet your future requests.

Data Parsing According to Bright Data

As you have just learned, choosing between building or buying depends a lot on your goals and needs. The ideal solution would be to have a commercial tool to help you build your own custom data parser. Fortunately, it exists and is called Web Scraper IDE!

Web Scraper IDE is a fully-featured tool for developers that offers ready-made parsing functions and approaches. This allows you to reduce the development time and helps you scale accordingly. Also, it comes with Bright Data’s unblocking proxy capabilities to allow you to scrape the Web anonymously.

If this seems too complex for you, keep in mind that Bright Data comes with a Data as a Service offer. Specifically, you can ask Bright Data to provide you with a custom dataset tailored to your needs. This will be delivered on-demand or on a scheduled basis. Basically, Bright Data will get you the web data you need when you need it while taking care of performance, quality, and delivery. This makes data parsing even easier!

Conclusion

Data parsing allows you to automatically transform raw data into a format that makes data easier to use. This means saving time and manpower, as well as improving the quality of the resulting data. As a result, data analysis will become easier and more effective. At the same time, data parsing comes with some challenges, such as special characters and errors in input files. Therefore, building an effective data parser is not that easy. This is why you might want to buy a commercial data parsing solution, such as Bright Data’s Web Scraper IDE. Also, do not forget that Bright Data offers a vast selection of datasets ready to use.

Ella Siman
Ella Siman | Product Marketing Manager

Ella manages the Product Marketing for all web data products. She’s a full-stack marketer passionate about understanding how users behave and make purchasing choices. When she’s not connecting the dots between product, sales, and marketing, Ella loves traveling the world, skiing, eating, yoga, and taking care of her plants.

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