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The obvious question that comes to the mind of a person who is new to the field of data science would be what data scraping exactly is? Even I used to ask the same kind of questions to many when I was an amateur in the field of data science. Without further wasting any time, let’s discuss what exactly data scraping is?

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Data scraping, also called web scraping, is the process of collecting information from a website into a locally saved file. There are lots of relevant data that are present all over the web and data scraping is a method by which we can get our hands on that data. Of late e-commerce websites have gained immense popularity and people prefer to buy products through these e-commerce websites. E-commerce websites such as Amazon, Flipkart, Snapdeal, Shein, Anotah, etc have been successful in attracting a lot of customers. With each click, a user makes in an e-commerce website an equivalent amount of data gets generated. 

Manually collecting that information would be a time-consuming process and won’t be practically possible. Data scraping, on the other hand, helps you to collect such huge sets of data in a very short time span.  Let’s move onto understanding how to scrape data from an e-commerce website.

How to scrape data from an e-commerce website?

There are lots of web scraping tools available in the market and many companies are helping their customers to gain access to these data. Tools used by these web scraping companies make use of a web scraper to scrape the data. Before writing a scraper, one needs to understand what data is to be extracted. A web scraper is designed based on the structure of a website. Python, a high-level programming language, can be used to write scrapers. 

Depending on the requirements, we might need to have multiple parsers within the scraper to get the data. These web scrapers work depending on the structure of the website. What you do practically to scrape a website is, you will send a GET request to the site of which you want to extract the data, and the website will return you an HTML code. The next step is to parse the information from this HTML code into a normalized format. The choice of format depends on your preference. You can, later on, analyze these data to collect relevant information.

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What kind of data can you collect?

Image URL, product description, product name, price, and so on can be collected from the website. Product reviews from the website can also be collected. These reviews will have useful pieces of information that can be used by various businesses to understand customer’s feedback or response towards their product. Customer sentiments towards a particular product or a brand can be figured out by analyzing these collected data. Based on these sentiments, customer segmentation and other forms of targeted marketing can also be achieved.

Challenges faced in web scraping

  1. Most websites won’t allow permission for scraping. Sites can disallow scraping through robots.txt files. If the website doesn’t permit scraping, it isn’t very easy to collect useful information from that particular website. In that case, it would be better to find an alternative site that allows scraping to get a similar kind of information.
  2. For each website, you need to write different scrapers. Web page structures of each site vary a lot; hence you need separate scrapers. Even a small update to the content of the website can lead to a change in the structure.
  3. You might have seen websites asking for CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) when you try to do so. These CAPTCHAs are easy for humans to understand but impossible for scrapers to bypass. There are lots of mechanisms available that can be implemented to avoid the CAPTCHAs, but still, it would end up making web scraping a time-consuming process.
  4. Websites usually make use of Honeypot traps to block scraping of data from their website. Websites place invisible links which are not visible for humans but are visible to the scrapers. By using these links, websites can identify the IP and block the scraping process.
  5. Data extraction on a large scale basis requires a large amount of storage space since lots of information will be generated from the scraping process. For large scale data extraction, storage space should be scalable and more secure.
  6. Quality of the data generated is very crucial, especially when you are planning to create various marketing strategies based on the data. Even data can change in the blink of an eye, so you should be very much careful to check if the data generated meets the quality guidelines even though it is challenging to achieve it.

There are lots of benefits or advantages for your business if data scraping methods are put into use. It is inexpensive and easy to implement.  Web scraping companies usually provide their services at a very low cost in a budget-friendly manner. Data scraping can also give faster outputs. A process that generally takes one week or more to if done manually can be completed at a more rapid rate. Still, for anyone who is not an expert in scraping, the process can get a bit confusing.

Simple errors in data extraction, especially which involve the collection of pricing details and so on can have a much greater impact later on. You might also require the need for an expert to analyze the generated data and convert it into a readable format. Even with all the limitations and challenges, the data scraping industry is still in its infant stages, and we can expect a huge rise in the demand for data scientists in the years to come.

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