Data parsing is a part of a data scraping process that turns raw data and information into a format that can be read and understood. This powerful programming solution helps us solve a unique problem in the modern world – we have so much public data at our disposal that it becomes difficult to use the knowledge without systematized extraction and analysis.
In this article, we will discuss the basics of data parsing, its types, and applicability. Depending on your data parser, you will have to familiarize yourself with its intricacies. Before focusing on data parsing, make sure to learn the basics of python programming.
Most parsing frameworks will be built on this language, and a better understanding will do wonders for your potential data science career. If you still have doubts, let’s take a deeper look at data parsing and its benefits.
Types of data parsing and its applicability
The data parsing process begins with lexical analysis, performed by a lexer – a structural component that initiates the process by scanning data and organizing its types into tokens. Then, a parser performs syntactic analysis, where the separated segments get assigned to a structure that rebuilds the associations between tokens controlled by syntax rules.
There are two main types of data parsing. Top-down parsers begin their work at the first symbol of the syntax and spot out the root of the syntax tree before moving to the latter components. Bottom-up parsers work in an opposite manner – by constructing a parse tree from its leaves and progressing towards the root.
While the theory of parsing might confuse beginner programmers, everything becomes clearer through examples and their applicability. Parsers are versatile tools that speed up and improve numerous technological solutions.
Arguably the most useful tools on the internet – search engines parse information from their web crawlers to create the most beneficial and convenient browsing experience.
Why parsing is necessary for data extraction
In today’s business environment, information is the fuel for progress and innovation. Tools that help us control and analyze inhuman amounts of data keep giving us a better understanding of the ever changing world with much higher precision.
With such knowledge, companies keep improving because they better understand the behavior of clients, competitors, and other internet users that can become potential customers. Data not only helps us figure out what the world wants and needs, but also improves the complexity and functionality of machines. The more information we have, the higher levels of convenience we can reach.
But the data extraction process has its fair share of obstacles. The public information on the web is presented in HTML to make it readable and presentable on a browser. Unfortunately, this format does not do any justice to a web scraper. In order to use the collected data, it has to go through a parsing process.
And the process isn’t always pretty. Data parsing is the least exciting part of data aggregation that requires the most resources and user participation. While simpler websites present far fewer challenges for efficient data extraction, the bigger fish need dynamic, sometimes multiple parsers to reorganize data into a usable format.
While writing code for parsers is an opportunity for beginner coders, the task is far from exciting. Without dedication and a sense of purpose, data parsing can discourage young programmers from pursuing a career in data science because the monotonic process does little to build new coding skills.
Automation is a great solution to many time-consuming problems, but despite its simplicity, data parsing is a frustrating and unpredictable task. Even if you put in the effort to complete the necessary steps to scrape your target, there are plenty of potential web page changes that can sabotage your parser.
While junior programmers may cherish the opportunity to start their career with data parsing, time is just as important as information is. Fortunately, the latter resource may contribute to speeding up parsing.
While no one today can predict website changes and automate parser adjustments, machine learning should lead to the development of AI that identifies website changes and automates parser adaptation. Such powerful solutions would eliminate the need for monotonous work that is necessary to maintain continuous data extraction.
Should you buy or develop your own parser?
A matter of debate amongst business owners, the solution to this dilemma depends on your perspective. Tech-savvy companies prefer to write their parsers because they have enough technical proficiency to satisfy their needs and maintain full control of the data extraction process.
If you plan to scrape multiple competitors that like to implement changes to their websites, your parser can be updated much faster to adapt and continue web scraping without interruptions.
But in the long run, building and sustaining your parser will require more resources.
Good servers, trained developers, and maintenance costs will only be valuable if your business depends on large-scale web scraping. For smaller tasks, a stable parser provided by professional suppliers will help you save money and time. Data parsing is not the most pleasant part of web scraping, it is a crucial process that helps us analyze and use huge amounts of data to our advantage.
While it may not be a priority for up-and-coming programmers to engage in such monotonous work, data parsing builds a better understanding of data science, its strengths, problems, and potential solutions. Even if you are not interested in web scraping and data analytics, knowing the basics will do wonders for your future programming career or business development.
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