Data parsing is a crucial process that empowers efficient data extraction. It allows us to transform the data collected with web scrapers into a manageable and understandable format. Data parsing is necessary for proper refinement and analysis that turns information into valuable and applicable knowledge.
Information possesses an even greater power in the modern world. One person can reach more data than a human brain can handle. Because we always strive for a greater level of efficiency, we rely on technology to simplify data extraction and analysis. Knowledge is power, but when the power is overwhelming to our primitive nature, we bend the rules to store and reorganize information to reap its benefits.
To speed up our progress, we develop information technologies that use more efficient ways of communication and data preservation. To get the greatest value out of our digital systems, we must construct the methods of extraction that can communicate with web servers and aggregate their public data.
When we use web scrapers to collect information, the initial product is a code that is unsuitable for analysis. In this article, we will explain the basics of the data parsing process, how it organizes data and makes it usable. As a beginner data scientist, you must also understand parsing errors and the necessary tools that empower the process of data aggregation. Smartproxy is a popular provider of proxy servers that protect and streamline information extraction. Check them out if you want to learn more about proxies, their types, and their applicability. For now, let’s try to understand data parsing and parsing errors
How data parsing detangles our data
As mentioned before, technological communication creates obstacles that make understanding extracted data more difficult. The information we see online is written in a code that gets rendered on a browser. By reading public information on these pages, you are a multitool that collects, analyzes, and stores information in your brain. Because our capabilities to complete these steps are very inefficient, we pass them on to separate technological entities.
Because the chain of these processes only lets us extract the code, we need another step – data parsing. When web scrapers extract the desired public information, we parse it into an understandable format.
Web scraping and other tasks are interesting and attractive due to their automation potential. Automating your access to knowledge is very efficient. Unfortunately, data parsing is a stubborn obstacle that sabotages the automated flow of information.
Parsing is an opportunity and a burden for young programmers. Writing code for parsers requires the most resources and maintenance. For example, if a company tries to extract data from multiple competitors, different websites might require unique parsing solutions, and the slightest changes can cause parsing errors. Building and maintaining your own parser is a monotonous process that can frustrate young programmers due to a lack of skill and flexible engagement.
Most parsing errors come from the unpredictable nature and differences between targeted web pages. If you are a young programmer yearning for a career in data science, data parsing is a great stepping stone that will require persistence but will open you a window to new opportunities.
How do I start learning about data parsing
Python is the most popular programming language with multiple free, open-source parsing frameworks to enrich your learning experience. You will encounter many tutorials on the internet that will help you familiarize yourself with the process.
If you are a complete beginner, do not panic! With so many sources, it is easy to build foundational Python knowledge that will help you wield the tools at your disposal. By following the most basic tutorials, you will soon understand the simple syntax of the language. If you want to keep the process interesting, you can test and tinker with the code written by other programmers and analyze its functionality.
But the biggest leap in programming knowledge comes from the desire to fulfill your idea. Organizing a personal project will help you find the necessary sources of data and their future appliance.
Do not make it complicated just yet. Just like regular connections to a web server increase its load, web scrapers can send way more requests that can slow down the targeted page. To protect the website and ensure stability, owners often filter recognizable connection requests and blacklist their IP addresses. Once you develop your data scraping and parsing skills, you can learn about avoiding such limitations with proxy servers. For now, simplify the process by targeting websites that do not restrict scraping, so you can focus on fluid extraction and analysis.
Once you read this article, you should understand the importance of data scraping and its challenges. Analyze parsing services provided by third parties to businesses or companies that build their own parsers, but the experience that comes from your attempts and parsing errors is the best teacher.