How Does a Web Crawler Work?
With the advent of the digital age and its unprecedented source of data, both individuals and organizations alike wanted to do their best to capitalize on web data. The first step towards doing so, however, was collecting and structuring the data so that it could be used for higher-level analysis.
Unfortunately, the many different types and standards of raw data posed a huge challenge to organizations and individuals looking to web data for insights. Raw data on the web is constantly evolving, exponentially increasing and growing in complexity. Traditional crawling approaches also aren’t able to crawl data at scale. Researchers and organizations that rely on web data must have a solution that gives them a way to quickly structure, standardize, and normalize the data.
Advanced web crawlers extract, enrich, and structure data so that the data is normalized and standardized and organizations can focus their resources on gaining insights instead of preparing data. These advanced web crawlers extract basic elements in a web page like the title of the article, the URL, and the body of the text or any external links. Then it infers additional data that is not explicitly mentioned in the raw data, like the publication date, language, country, and author. Finally, it enriches fields that require a deeper layer of meaning. For instance, how do we know when the word “fox” refers to an animal, versus the news channel or Back to the Future star Michael J. Fox?
A python web crawler is one of the most popular types of web crawlers because it is known to extract data most efficiently and at scale. For experienced programmers, the python programming language lets you get started quickly. A python web crawler is also the best choice if you want to integrate your machine learning algorithms into another interface that uses a different code. It also has a more mature external library for use in machine learning applications and is easier to maintain in the long run.
Webz.io is a web crawler that delivers organizations’ machine-readable, structured data feeds in JSON, XML, or Excel format at scale. It crawls hundreds of thousands of web sources, extracting raw data and transforming it into structured, inferred, and enriched information to some extent. Both researchers and enterprise-level organizations alike rely on Webz’s web crawler for its ability to deliver accurate, high-quality data for a wide range of use cases in multiple verticals.
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