How to Extract Public Data
Information collection and extraction are the forces that drive progress and innovation. With the successful accumulation of knowledge, talented individuals have created new products, services, or a competitive advantage over other parties in the market.
The problem with acquiring information in the past was resource guarding. Successful parties have used their access to knowledge to establish and consolidate superiority over others and ensure more stable and successful lives. By analyzing historical events that influenced human lives the most, we can see that revolutionary inventions and products come to fruition at a far greater pace when valuable, educational public data becomes accessible to a larger percentage of the world population.
The rate of progress keeps rising when barriers based on not only status but gender, race, and other factors are eliminated. Our most impressive achievements have occurred over the last decade, primarily thanks to a free movement of information.
The main contributor that gave access to unfathomable amounts of free public data is the internet. Elon Musk has made a great observation that modern humans are already cyborgs – most people cannot imagine their lives without smart devices, which can be seen as detachable parts that make us infinitely smarter.
In this article, we will talk about the ways to make the process of data extraction more efficient. While you can manually visit websites to learn and use the acquired knowledge for your goals, web scrapers are powerful tools that can speed up, organize, and automate this process. We will also discuss Data parsing that allows us to structure information in an understandable format and the purpose of proxy servers, as well as their assistance in data aggregation. Check out Smartproxy – a reliable provider, to learn more about data parsing and proxy use for web scraping in their blog articles.
Can you have too much data?
The abundance of information has its downsides. The accessibility of information makes us lazier. Most internet users spend their time on the web looking for entertainment. More sources of public data also bring a ton of well-tailored distractions. The other disadvantage comes from the amount of accessible information. When everyone can utilize this knowledge to build proficiency in a preferred niche, more data is required to maximize precision.
But no human can process this much data. Tech-savvy businesses and individuals use web scrapers and data parsing to extract and organize information to turn it into the most accurate knowledge by bypassing human limitations. You can have too much data due to your biological limits. When you have technology that performs these tasks with supreme efficiency and no restrictions on storing information, data is just fuel.
How to start extracting data
Universities around the world recognize the value and potential career in data analysis and other niches that intertwine with the process. Most use Python and its open-source frameworks so students can learn the basics and implement new skills in various tasks or their personal projects.
You can find many tutorials and courses that focus on data scraping with Python. If you are a complete beginner, building up the coding skills should not take much time. The process makes much more sense when you have examples of well-written, efficient code that gets the job done. Eventually, the best teacher will be your own experience. Figure out a project that you are passionate about which would benefit from data. Passionate about basketball games? Use web scraping to extract information for statistical analysis to understand the game better.
What about data parsing?
Data parsing is an essential step that allows us to read and understand extracted information. While web scrapers are much more efficient at aggregating, human brains have some advantages in adaptability and multitasking. While you can absorb public data and turn it into knowledge simultaneously, we have to separate these processes to accelerate data extraction with technology.
Because web scrapers extract data in code, data parsing helps us disentangle it to make information usable. BeautifulSoup is a popular Python scraping framework that can help you familiarize yourself with the parsing process.
However, data parsing is a simple process in essence, but it has its unique complexities. Because web owners use different practices to develop an attractive page, parsers need adjustments to properly restructure data. Unlike web scraping, automating data parsing is extremely difficult because it is impossible to predict changes and differences in targeted websites.
Why do you need proxy servers?
Web scrapers establish connections with web servers to extract data, which exposes your IP address. You will encounter a lot of retailers, big tech companies, and other web owners that protect their websites from web scrapers. The best way to make sure that your data extraction procedures continue without interruptions is to send them through proxy servers supplied by a business-oriented proxy provider. When your IP is protected, you can optimize your web scrapers and get the maximum value from these tremendous tools. Automate your data extraction with proxy servers to eliminate interruptions.