What is Web Scraping?

Web scraping is known by many other names, depending on how a company prefers to call it: Screen Scraping, Web Data Extraction, Web Harvesting, etc. It is a technique used to extract large amounts of data from websites.

Web scraping is a process where data is extracted from various websites and repositories and saved locally for immediate use or analysis that will be conducted later. The data is stored in a local file system or in database tables according to the structure of the extracted data.

Most websites we regularly visit only allow us to view content and generally do not provide copying or downloading capabilities. Manually copying data is akin to cutting newspapers, and it can take days and weeks.

Web scraping is a technique to automate this process, where an intelligent script can help you extract data from selected web pages and save it in a structured format.

Web scraping software automatically loads multiple web pages one after another and extracts data according to requirements. It is either custom-built for a specific site or can be configured based on a set of parameters to work with any site. With one click of a button, you can easily save the data from the site into a file on your computer.

In today's world, intelligent bots perform scraping. Unlike basic scraping that copies only what pixels display on the screen, these bots extract the underlying HTML code as well as data stored in the database in the background.

Some notable benefits of web scraping and what it is used for:

1. Collecting product and price information

Businesses scan e-commerce websites for prices, product descriptions, and images to obtain all possible data to enhance analytics and predictive modeling. Price comparison through scraping has become very important for businesses to know their competitors' rates in recent years.

If prices are not competitive, e-commerce sites can quickly go out of business. Even travel sites have long been extracting prices from airline websites. Custom web scraping solutions will help you get all the variable data fields you may need. Thus, you can collect data and create your own data warehouse for both current and future use.

2. No one on the Internet can hide

This helps collect data related to an individual or company. This data is then used for analytics, comparison, investment decisions, hiring, etc. Many companies today scrape job boards for such purposes.

3. Custom analysis and curation

Specifically designed for new sites/channels, where collected data can help understand demand and audience behavior. This helps new companies, to start with, choose activities and products based on discovered patterns that will receive more organic visits. Thus, they will have to spend less on advertising.

4. Online reputation

Online reputation is very important today, as many businesses rely on word of mouth that helps them grow. Here, scraping from social networks helps understand current public opinion and sentiment.

Then the company can do even small things that have a large social impact. Influencers, trending topics, and demographic facts can be identified through data collection and then used so that the company can restore its image or obtain a higher "public satisfaction" rating online.

5. Identifying fake reviews

Online reviews help modern online shoppers decide what and where to buy, whether a refrigerator or a car. Consequently, these reviews are of great importance.

Opinion spam refers to "illegal" activities, such as writing fake reviews on portals. It is also called shilling — activity aimed at deceiving online shoppers. Thus, website scraping can help find reviews and determine which ones should be blocked and which should be verified, since such reviews usually stand out from others.

6. Targeted advertising based on customer sentiment

Scraping not only provides numbers for counting but also helps a company understand which ads are more suitable for certain internet users. This helps save marketing revenue while attracting views that often convert.

7. Business-specific scraping

Businesses can get more services in one place to attract more customers. For example, if you open an online health portal and collect and use data about all nearby doctors, pharmacies, nursing homes, and hospitals, you can attract many people to your site.

8. Content aggregation

Media sites need to instantly get fresh news as well as other relevant information that people browse online. Often, sites that publish news first get the most views. Scraping helps monitor popular forums as well as capture trending topics and much more.

Automated scraping methods have come a long way:

1. HTML parsing:

HTML parsing, the most common of tasks, can be performed with JavaScript and target linear and nested HTML pages. This fast method allows identifying HTML scripts on websites, which previously might have been done manually, and is used to extract text, links, screen scraping, backend data, and much more.

2. DOM parsing:

Content, style, and structure of an XML file are defined in the DOM, short for Document Object Model. Parsers that need to know the inner workings of a web page and extract scripts running deep inside that have been abstracted typically use DOM parsers.

Specific nodes are collected using DOM parsers, and tools like XPath help crawl web pages. Even if the created content is dynamic in nature, DOM parsers come to help.

3. Vertical aggregation:

Companies with huge computing power focused on specific verticals create vertical aggregation platforms. Some even deploy these platforms to collect data in the cloud.

On these platforms, bots for specific verticals and enterprises are created and monitored, with practically no human intervention required. The existing knowledge base for the vertical helps efficiently create bots for it, and the performance of bots created this way tends to be much higher.

4. XPath:

XML Path Language or XPath is a query language used when extracting data from XML document nodes. XML documents have a tree structure, and XPATH is a simple way to access specific nodes and extract data from those nodes. XPath is used together with DOM parsing to extract data from websites, regardless of whether they are static or dynamic.

5. Text pattern matching:

This is a regular expression matching technique (commonly called regex in the coder community) using the UNIX grep command. It is commonly used in popular programming languages such as Perl, and more recently Python — Beautiful Soup.

There are many programs and services for scraping available on the market, and there is no need to be a master of all the techniques mentioned above. There are also tools like CURL, HTTrack, Wget, Node.js, and others.

Different approaches to scraping

1. DaaS or Data as a Service

Outsourcing your web data extraction needs to a data service provider is the most recommended and straightforward way to satisfy your business's data hunger. When a data provider helps you with data extraction and cleaning, you no longer need to maintain a separate team for data issues and can rest easy.

They will handle both the software and infrastructure needs required by your company for data extraction, and since these companies handle data extraction for clients on a regular basis, you will never have a problem they haven't solved or at least encountered before. All you need to do is provide them with your requirements and then sit back while they work their magic and deliver your invaluable data.

2. In-house web scraping

If your company is technically rich, you can also opt to create an in-house data scraping department. You will need not only skilled professionals who have worked on web scraping projects, experts in R and Python, but also a cumbersome infrastructure that must be set up so your team can scrape websites around the clock.

Scraping scripts tend to break even with the slightest change to the web pages they target, and therefore scraping is never a "set and forget" solution. You need a dedicated team constantly working on solutions, and sometimes they may anticipate major changes in how data is stored on web pages, and then they must be ready for it.

Building and maintaining a scraping team is challenging and should only be undertaken if your company has sufficient resources.

3. Vertical-specific solutions

Data providers that serve only a specific industry vertical exist in vast numbers, and these vertical-specific data extraction solutions are excellent if you can find one that meets your data needs. Since your service provider will work only in one area, there is a chance they will be extremely skilled in that field.

Data sets can vary, and the solutions they can provide can be very flexible depending on your needs. They can also offer different service packages based on your company's size and budget.

4. DIY scraping tools

For those who have neither the budget for an in-house scraping team nor the ability to use a DaaS provider, there are DIY tools that are easy to learn and simple to use.

However, a serious drawback is that you cannot extract too many pages at once. They are often too slow for mass data extraction and may not be able to parse sites using more complex rendering techniques.

How scraping works:

There are several different methods and technologies that can be used to create a scraping script and extract data from the web. Below is the basic structure for building a scraping project.

1. Seeding

This is a procedure similar to traversing a tree structure, where the script first goes through the primary URL, then looks for the next URL in the data obtained from the base URL, and so on. The initial URL is hardcoded at the very beginning. For example, to extract all data from various pages of a website, the initial URL serves as the foundation.

2. Direction identification

After extracting data from the initial URL and saving it in temporary memory, the hyperlinks present in the data must be passed to the script, and then the system should focus on extracting data from them.

3. Queuing

The scraper must extract and store all pages it analyzes during traversal in a single repository as HTML files. The final stage of data extraction and cleaning occurs in this local repository.

4. Data extraction

All data you may need is now in your repository. But this data is not usable. So you need to teach the scraper to identify data points and extract only the data you need.

5. Deduplication and cleaning

Primary data must be extracted, and duplicate records must be removed by the script automatically. Such features should be built into the scraper's intelligence to make it more user-friendly and the output data more usable.

6. Structuring

Only if the scraper is capable of structuring unstructured data will you be able to create a pipeline to directly feed the scraper's output into your business.

Best practices for web data scraping:

Although this is an excellent tool for obtaining information, there are several legal aspects you should take care of to avoid trouble.

1. Respect robots.txt

Always check the Robots.txt file of the site you plan to scrape. This document contains a set of rules that define how bots may interact with the site, and scraping in violation of these rules can lead to lawsuits and fines.

2. Stop hitting the server too often

Don't become a "frequent visitor." Under heavy load, web servers may eventually hang. Bots increase the load on the site's server, and if the load exceeds a certain limit, the server may become slow or crash, ruining the site's excellent user experience.

3. It's better to scrape data during off-peak hours.

To avoid high web traffic zones and crashing the server.