What is Web Scraping?

In today’s world, data has become the most valuable asset. Using the right data enables businesses and scientists to make better decisions. The question then becomes where to find useful data. This is where “Web Scraping” comes in.

Web scraping means getting data from websites in a structured and organized format. This data set can be sourced from multiple different webpages, and is often of a very large size. This process can also include cleaning up and transforming the data in a suitable format. Web scraping can benefit people in all lines of work, particularly data scientists, business analysts, and marketers.

What makes Web Scraping very important today is the fact that the entirety of the world’s knowledge exists on the Internet. In most cases, each individual piece of data is stuck on a web page. In order to process the data sets, data scientists need to gather each of the little pieces and put them all together in a usable format.

My experiences have taught me that companies rarely need data from a single source. Often, the data lives on different websites, and in different formats. One of the biggest challenges of web scraping is to collect and transform data into a uniform manner before it can be used properly.

After years of helping companies in various industries, I have seen the different approaches companies follow to gather data in today’s world.

Manual Data Gathering

Believe it or not, there are many companies who hire employees specifically to manually gather data from the internet. The primary role of these people is to browse websites manually, and copy/paste data from one or more websites into a spreadsheet or form on a daily basis.

There are many disadvantages with this approach including: paying for labour, lower accuracy of data, and time constraints, to name a few. Although this is not a preferred approach, many companies go this route, mainly due to them being unaware of better solutions.

Custom Scripts

Companies and data scientists who are willing to invest time and money may decide to write their own custom scraping scripts for each website. This approach requires a software developer to write custom scripts for each website, page by page. Although this approach is much faster and more accurate than the manual approach, it requires development time which is very expensive for any company or individual. Since you are writing your own custom script, handling the data and the web scraper will be in your hands and it will be flexible enough to meet any of your specific requirements.

Due to different HTML structures on different domains, the developer needs to spend a ton of time figuring out the right approach to scrape the data from each web page. Keep in mind that even a very good developer will have a hard time scraping some of the Javascript heavy websites.

Web Scraping Tools

These tools are designed specifically to get large data sets from websites, and are usually compatible with most websites. This means after learning how to work with the web scraping tool, you can use it on any website and scrape your data on a regular basis.

Keep in mind that some of these tools are technical and require coding knowledge. However, some of the web scraping tools are designed to be used by non-technical users, and thus most computer users can learn to work with them in a short period of time.

Similar to any approach, there are a few pros and cons to this approach. Web scraping tools are great for any company or individual who does not want to spend a lot of time and money to get accurate data from websites. This approach also eliminates the need to hire people with programming skills, and the time needed to write custom scripts. However, due to the tool being being a generic web scraper, you might face some challenges customizing the tool for your specific desired format. This means that you should do some research before choosing your web scraping tool and spending time learning how to work with it.

I list a few important requirements when it comes to choosing a web scraping tool:

1. Flexibility in scraping different HTML formats: for example, you want to make sure that the web scraper is flexible enough to handle Javascript (Ajax) on websites.
2. Ability to generate clean structured data: your data shouldn’t require a lot of post processing before being useful.
3. Data formats: accessibility of the data through different formats (excel, json) and APIs.
4. Running the web scraper on the cloud: you shouldn’t need to dedicate your own servers for your web scraping.
5. Ability to bypass bot detectors: the web scraping tool should have access to a pool of IP addresses in order to gather data from websites that block requests from bots.
6. High performance: ability to offer high scraping speed in order to gather data in a short amount of time.
7. Great support: when it comes to choosing the right application, you should always consider the company’s quality of support to make sure that you are in good hands if something goes wrong.

Choosing the right approach to web scraping will involve looking at your specific situation, such as your coding abilities, and the amount of resources, time, and money you have available. In general, the first approach is often the worst approach, due to the reasons mentioned above. Many companies or data scientists with high technical knowledge may decide that the second approach works best for them. However, after a few months, they decide to go with the third approach, due to the realization that the difficult web scraping challenges they are trying to tackle have already been solved by companies that have spent years exclusively perfecting their web scraping tools.

If you are thinking of using a web scraping tool, a quick Google search will provide you with several great web scraping tools. Make sure you go over the list of important requirements I mentioned above, before you invest your time and money into the tool.

About the Author

Hoda Raissi is COO of ParseHub, a visual web scraping tool that can get data from any website. It is designed to be used by non-technical users, and can help them extract large data sets in minutes. Hoda has years of experience working with different researchers and companies that need data to get their job done.

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