The internet we know and love today is thriving due to the free movement of ideas and information. The web and its most fascinating attractions exist because such connections of computers, servers, and digital devices create perfect, ever-growing storage of data and the means for communication.
The amount of knowledge on the web can be incredibly useful, but for one person, such an abundance of data is overwhelming.
On the web, we have more information that a single human could reach and process in many lifetimes. Still, knowledge remains our most powerful resource that generates progress, but how do we tap into the potential carried by big data?
To harness the true power of these technological solutions and inventions, we empower the same tools to empower a much faster retrieval of information.
With little programming knowledge, we can create web scrapers – data aggregators that send data requests to websites of interest and extract their HTML code. Instead of manually visiting every page, we automate the process with web scraping bots.
But scraping is only the initial step of data aggregation. What good does an HTML code bring for our research? If anything, it just presents the same information that we can already observe through a browser.
To filter out and extract valuable information and order it into readable and understandable data, it has to go through the process of data parsing.
In this article, our goal is to introduce non-tech-savvy readers to data parsing and its forms. Parsing is an essential process for modern businesses that use information extraction to improve and enhance companies and their pursuit of progress.
We will talk about programming languages that let you build your parsers. For example, Python has lxml and other libraries that simplify the data parsing process.
Evaluating lxml, Beautiful soup, and other tools will help us evaluate the pros and cons of building your scraper, as well as understand the cases where outsourcing these tasks might be a better idea.
Last, but not least, we will address the role of proxy servers in the process of data aggregation.
To learn more about proxies, their necessity in scraping, and how to use libraries like lxml, check out Smartproxy – a business-oriented proxy server provider that provides educational material to their clients and interested users.
The start of data parsing
Once we have the extracted HTML code files from visited websites, we transform the structure into a readable and understandable format. We achieve this goal with the help of data parsers.
What is a data parser?
Data parsers are tools that transform the unreadable code by extracting valuable bits into organized tables or JSON files.
Most parsers have two structural components – a parser does the heavy lifting and builds the final structure of extracted data, and the lexer – an inspector that compartmentalizes information from an HTML code into tokens.
Two parsing strategies reconstruct obtained documents into logical trees. Top-down parsing starts from the first data symbol, identifies the syntactic root, and goes down to structural elements.
Bottom-up parsers go through a reverse process to detect presented content, recognize the root of the tree, and build up to the first symbol. In the end, a successful parser has to reform the extracted HTML code into a readable and understandable format.
Data parsing problems
Automation is the key to successful and efficient data extraction tasks. Aggregating an HTML code from a chosen web server is a simple task that can be easily accelerated with automation.
Data parsing, however, has far more challenges that can sabotage the correct organization of information. Website owners use many tools to fulfill their vision of a unique and attractive page that meets the needs of its visitors.
Different building blocks create unique pages that may not react to your written parser. Even minor structural changes can stop the parsing in its tracks.
This makes data parsing the most resource-intensive part of data aggregation – because it cannot be fully automated due to the unpredictable nature of targeted websites, coders that operate them have to make constant adjustments to create parsers that fit the requirements and deliver an obstructed final product.
Pros and cons of building a data parser vs buying one
Creating your own parser gives you complete control over the process: the ownership lets you make rapid adjustments without stagnation.
When you have constant access to your parser, immediate customization will help you overcome the obstacles and extract valuable information faster. When you have qualified employees that can build and maintain data parsers, creating your parser is cheaper than buying it.
While building your parser for business or individual tasks has its strengths, we must also discuss weaknesses that can have crippling results for some companies that do not have the resources to maintain them.
The first and obvious one is the cost of maintenance. Making constant changes to your parser to ensure its effectiveness is a necessary process that can require a lot of manual labor by company coders.
Some businesses do not have the luxury to employ IT-related personnel to take care of these tasks. Even if you want to modernize your company, performing these monotonous tasks will still require additional training of your employees to effectively implement these changes.
The choice of buying or building a parser depends on the resources of your business and their allocation. Companies that have their business model centered around IT and data science will have a far easier time building and maintaining their parsers.
Understanding the process of data parsing will help you decide when you can organize these tasks yourself and when it may be wiser to outsource them to a professional.