Introduction to Web Scraping
Web scraping is a technique to fetch data from websites. While surfing on the web, many websites don’t allow the user to save data for personal use. One way is to manually copy-paste the data, which both tedious and time-consuming. Web Scraping is the automation of the data extraction process from websites. This event is done with the help of web scraping software known as web scrapers. They automatically load and extract data from the websites based on user requirements. These can be custom built to work for one site or can be configured to work with any website.
Uses of Web Scraping: Web scraping finds many uses both at a professional and personal level. Having different needs at different levels, some popular uses of web scraping are.
- Brand Monitoring and Competition Analysis: Web Scraping is used to get customer feedback regarding a particular service or product so as to understand how a customer feels regarding that particular thing. It is also used to extract competitor data in a structural, usable format.
- Machine Learning: Machine Learning is a process of Artificial Intelligence in which the machine is allowed to learn and improve with its experience rather than being explicitly programmed. For that, a large amount of data is required from millions of sites which is extracted through web scraping software.
- Financial Data Analysis: Web Scraping is used to keep a record of the stock market in a usable format and hence employ the same for insights.
- Social Media Analysis: It is used to extract data from social media sites to gauge customer trends, and how they react to the campaign.
- SEO monitoring: Search Engine Optimization is the optimization of the visibility and ranking of a website among different search engines like Google, Yahoo, Bing, etc. Web scraping is used to understand how the ranking of the content over time.
An there are so many other reasons to use Web Scrapping.
Techniques of Web Scraping: There are two ways of extracting data from websites, the Manual extraction technique, and the automated extraction technique.
- Manual Extraction Techniques: Manually copy-pasting the site content comes under this technique. Though tedious, time taking and repetitive it is an effective way to scrap data from the sites having good anti-scraping measures like bot detection.
- Automated Extraction Techniques: Web scraping software is used to automatically extract data from sites based on user requirement.
- DOM Parsing: The Document Object Model is the official recommendation of the World Wide Web Consortium. It defines an interface that enables a user to modify and update the style, structure, and content of the XML document.
- Web Scraping Software: Nowadays, many web scraping tools are available or are custom build on users need to extract required desiring information from millions of websites.
Tool for Web Scraping: Web Scraping tools are specifically developed for extracting data from the internet. Also, known as web harvesting tools or data extraction tools, they are useful for anyone trying to collect specific data from websites as they provide the user with structured data extracting data from a number of websites. Some of the most popular Web Scraping tools are:
Legalization of Web Scraping: The legalization of web scraping is a sensitive topic, depending on how it is used it can either be a boon or a bane. On one hand, web scraping with good bot enables search engines to index web content, price comparison services to save customer money and value. But web scraping can be re-targeted to meet more malicious and abusive ends. Web scraping can be aligned with other forms of malicious automation, named “bad bots”, which enable other harmful activities like denial of service attacks, competitive data mining, account hijacking, data theft etc.
Challenges to Web Scraping: Besides the challenge of the legality of web scraping, there are also other problems that pose a challenge to web scraping.
- Data Warehousing: Data extraction at a scale will generate a large amount of information to be stored. If the data warehousing infrastructure is not properly built then the searching, storing and exporting of this data will become a cumbersome task. Hence, for large-scale data extraction, there needs to be a perfect data warehousing system without any flaws and faults.
- Website Structure Changes: Every website periodically updates its user interface to improve its attractiveness and experience. This requires various structural changes too. Since the web scrapers are set up according to the code elements of the website at that time, they require changes too. So, they require changes weekly too to target the correct website for data scraping as incomplete information regarding the website structure will lead to improper scraping of data.
- Anti-Scraping Technologies: Some websites use anti-scraping technologies that thwart away any scraping attempt. They apply a dynamic coding algorithm to prevent any bot intervention and use the IP blocking mechanism. It requires a lot of time and money to work around such anti-scraping technologies.
- Quality of Data Extracted: Records that do not meet the quality of information required will affect the overall integrity of the data. Making sure that the Data Scraped meets the quality guidelines is a difficult task as it needs to be done in real-time.
Future of Data Scraping: As there are some challenges and opportunities for data scraping, it can be fairly deemed that the unintended data-scraping practitioners are prone to create a moral hazard where they target the companies and retrieve their data. However, since we are on the verge of data transformation, data-scraping in combination with big data can provide the company’s market intelligence and help them identify critical trends and patterns and identify the best opportunities and solutions. Hence, it won’t be wrong to say that Data scraping can be upgraded to the better soon.