What is Data Scraping? Learn Web Scraping

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Let’s say you need some data from a website, like the Donald Trump paragraph from Wikipedia. You could just copy and paste the information into your own record.

However, suppose you are looking for huge quantities of information from a site—for e.g. large amounts of data to train a machine learning algorithm. That’s when copying and pasting will not be useful!

This is when web scraping comes in handy. Unlike manually acquiring data from a source, which can take forever, web scraping utilizes intelligent automation tactics to get millions of datasets in much less time.

So let’s look at what web scraping truly is and how to use it to obtain information from other websites.

What is Data Scraping?

Extracting data in large amounts from the web pages can be done through mechanized methods means Web Scraping. Typically, this information is unordered and recorded in HTML format and is then reconfigured into consistent data to apply to different programs.

To carry out Web Scraping there are multiple approaches, like using online services, specified APIs, or constructing your own code for web scraping.

Many large websites offer APIs that permit users to access their data from sites in a systematic format, which is the ideal approach.

Nonetheless, some websites either do not provide access to all their data in an apparatus shape or may not have the advanced technology to do so; in such a case it would be best to perform Web Scraping to extract information from the website.

Web scraping necessitates two components: a crawler and a scraper.

The crawler is an artificial intelligence algorithm that explores the internet, searching for specific required data by following links across the web.

On the other hand, the scraper is a tool particularly crafted to obtain information from website pages. The design of the scraper will be determined by the complexity and scope of the project and should make it possible to rapidly and precisely extract the necessary information.

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Two Types of Data Scraping

1. Web Scraping

Content or web scraping is the leading type of data scraping used for commercial purposes. It utilizes software that automatically downloads, decodes the information on webpages or other resources, then passes it over to companies that will make use of it.

Intended for data study, retrieval, and investigation, web scraping has been familiar since the 2000s. Search engines made use of a kind of web scraper named “Web Crawlers” to look through the content and data on many websites.

The keywords were then indexed and implemented to give power to the search engines users employ to traverse the internet.

If there weren’t web crawlers, sites like Google, Yahoo!, and Bing would not exist. Web scraping is extensive, personalized, and strong at getting whatever modern internet data your business needs for adept decision-making.

A lot of businesses adopt web scraping, including:

  • Search Engines: Obtaining facts from websites to show concerning search criteria,
  • Sports: Following sporting games for data, fantasy standings, bets, and the like,
  • Government: Examining inflation, currency rate, or news happening in a particular country.
  • Real Estate: Monitoring prices of housing markets, houses available for rent or purchase situations in competition with one another and more.
  • Marketing: Examine social media sentiment regarding consumer confidence levels, SEO capabilities, metadata extraction, website content scraping, material extraction, possible influencers, and other details.
  • Pricing: Compare prices of tickets for flights, hotels and lodgings, accommodation, festivals, products along with goods or services to secure the best deal at an ideal cost.

2. Screen Scraping

Unlike web scraping, screen scraping does not download and analyze web sources. Instead, it analyzes visual elements—directly from the display designed for users—to scrape text, images, and other content, making it useful for research and application analytics.

Additionally, it is exceptionally valuable for looking through outdated sources. Technology advances quickly which leaves certain legacy systems, software, and programs obsolete and costly to continue using.

Moreover, these significant investments comprise private and important data that would be a hassle to export without the help of a screen scraper.

Screen scraping an entire system is critical for certain businesses, particularly when they need to preserve their information intact over long periods of time due to regulations or record-keeping reasons.

Screen scraping is suitable for extracting data without having access to its source code since numerous older CRM systems don’t have built-in APIs, which makes this technology a powerful tool for migrations due to its ability to access and extract old data with great precision.

A few of the various ways that businesses can use screen scraping include:

  • Utilizing regular APIs to study display contents,
  • Using system API interception to track (capture) how information reaches the screen,
  • Applying a custom mirror or accessibility driver,
  • Utilizing optical character recognition (OCR).

Businesses in various sectors employ web scraping to carry out day-to-day tasks, such as:

  • Crucial Legacy Systems: Utilized for the absolute and complete moving of all existing system data
  • Governments: Accessing public and government archives
  • Health Care Providers: Patient medical records
  • Banks: financial documents, account details, and financial transactions
  • Energy & Mining: Necessary legacy systems information, documentation and regulatory approvals etc.
  • Corporations and Multi-Nationals: Acquiring enterprise data from ERP, CRM, SCM, and more

Data Scraping and Cybersecurity

All kinds of businesses benefit from data scraping devices, not exclusively for malicious purposes. These applications include marketing research, intelligence gathering, web content and design, and personalization.

Nonetheless, data scraping also causes all sorts of complications for many companies since it can be utilized to reveal and misuse delicate information. The website being scraped may not be aware that their details are collected or what exactly is acquired.

Furthermore, a trusted data extractor might not secure the data properly, giving attackers an entryway to access it.

Should malevolent individuals gain access to the data collected by web scraping, they can manipulate it in cybercriminal assaults. For instance, assailants can employ scraped information to launch:

1. Phishing attacks

By exploiting collected data, attackers are able to become more effective in their phishing attempts. They can figure out which workers possess the access privileges they wish to go after or if someone is particularly vulnerable to a phishing attack. If assailants manage to determine the identities of higher-ups, they can conduct spear phishing attacks designed for their victims.

2. Password

Breaking attempts: hackers are capable of deciphering passwords to bypass authentication protocols, even if the passwords themselves haven’t been compromised.

They can look into openly available information about your employees in order to deduce passwords based on individual details.

Data Scraping Techniques

Here are some popular methods used to acquire data from websites. By and large, all scraping strategies retrieve information from web pages, process it with a scraping engine, and create one or more data files featuring the taken content. Here are some data scraping tools:

1. HTML Parsing

The process of HTML parsing necessitates the use of JavaScript to target either a linear or nested HTML element document.

It is a swift and robust technique for harvesting text and links (like a deep link or an email address), screenscraping, and collecting assets.

2. DOM Parsing

The Document Object Model (DOM) outlines the formatting, design and content of an XML file. Generally, scrapers make use of a DOM parser so as to perceive the structure of websites more profoundly.

DOM parsers can be brought into action for accessing nodes that hold facts and harvesting the website through techniques such as XPath.

To harvest dynamically generated content, scrapers may activate web browsers like Firefox or Internet Explorer to take out entire web pages (or portions of them).

3. Vertical Aggregation

Businesses that rely on a great deal of computing power can construct vertical accumulation platforms to focus on certain verticals.

These are information-gathering stages that can be operated on the cloud and are used to create and track bots for special verticals with a minimum of human input.

Bots are produced in keeping with the data needed for every vertical, and their effectiveness is established by the caliber of data they retrieve.

4. XPath

XPath stands for XML Path Language, a type of language that is used to inquire information from XML records. Specific characteristics of an XML document are similar in form to a tree structure and so, scrapers employ XPath to move around by choosing nodes contingent on various criteria.

A scraper may couple DOM parsing with XPath to draw out complete web pages and distribute them on the intended site.

5. Google Sheets

Google Sheets is a widely used tool for web scraping. By incorporating its IMPORTXML feature, scrapers can collect information from a website, which can be useful when attempting to obtain applicable patterns or figures from the site. This feature also permits them to determine whether a website can be scraped or is immune to it.

Data Scraping with Dynamic Web Queries in Microsoft Excel

Establishing an interactive web query in Microsoft Excel is a convenient, varied data scraping process that lets you configure a data feed from an outside website (or multiple websites) into a worksheet.

Use the steps listed here to learn how to take information from the web and put into Excel:

  • To bring in data into an Excel workbook, start by selecting the cell to import it into, then click the “Data” tab.
  • Next, opt for “Get external data,” then find and hit the web symbol.
  • Look for the minuscule yellow arrows nearby and across certain contents of the website.
  • Paste the URL of the web page you need to import data from in the address bar (we suggest settling on a site where info is listed in tables) and press “Go.”
  • Select the yellow arrow beside your desired data, hit “Import,” and then an “Import data” dialogue box will show up.
  • Hit “OK” or adjust your cell pick, if desired.

By adhering to these instructions, the web data should now be visible in your spreadsheet.

The best thing about dynamic web queries is that they don’t just lead to your spreadsheet receiving the data one time – it is continually fed in with the newest version of information from the origin website.

This is why we term them “dynamic.” To adjust how often your dynamic web query updates the imported records, go to ‘Data,’ then “Properties,” and pick a frequency.

web scraping

What Is Web Scraping Used For?

Web Scraping can be found in multiple sectors. Let us examine some of them!

1. Price Monitoring

Companies can make use of web scraping to acquire the data from web of their own goods as well as those of their rivals in order to comprehend its implications for their pricing plans.

With this data, firms can fix the pre-eminent price for their products so they can gain the greatest amount of revenue.

2. Market Research

Enterprises can utilize web scraping for market research. The observation of massive amounts of excellently scraped web data from the website can greatly aid businesses in recognizing customer inclinations and determining which route they should take.

3. News Monitoring

Drawing out details from news websites can provide a business with sweeping, up-to-date reports. This is even more important for firms that appear frequently in the news or require daily news updates to continue their operations. Of course, just one day’s news report can decide a company’s fate!

4. Sentiment Analysis

In order to get an idea of the public’s sentiment concerning their products, companies need to take advantage of Sentiment Analysis.

Through online scraping, companies can gather data from platforms like Facebook and Twitter about how individuals feel about their merchandise. Using this information helps these companies cater to what people desire and overtake their competition.

5. Email Marketing

Companies may also apply web scraping for email marketing. By utilizing web scraping, they can obtain email IDs from a variety of sites and then send bulk emails for promotion and marketing to all the people who have those email addresses.

6. Gathering disparate data

An enormous benefit of data scraping is that it can assist you in collecting assorted data into one centralized area. By crawling, we can access unorganized, scattered information from multiple sources and consolidate it all into one feed and make it structured.

If different entities manage several websites, you are able to join them all together in a single location.

7. Outputting an XML feed to third-party sites

It is essential to automate the time-consuming process of revising your product particulars if your inventory tends to fluctuate frequently.

Data scraping is a significant usage in e-commerce for this purpose, especially to furnish Google Shopping and other external merchants with product information from your website.

How to Scrape the Web?

Clearly, we comprehend what a web scraping bot does. But it’s more than just running code and hoping it works! In this area, we’ll look at every step that needs to be taken.

Since the procedure for doing these steps relies upon which tool is in use, we’ll predominately discuss the fundamentals.

Step 1: Find the URLs you want to scrape

The primary step you need to take is determining which website(s) you would like to scrape. For example, if you are researching customer book reviews, your selections could include sites such as Amazon, Goodreads, or LibraryThing.

Step 2: Analyzing the page

Prior to programming your web scraper, you must recognize what it is going to scrape. You can capture a glimpse of the backend code, which the scraper will observe, by right-clicking any element on the website and opting for ‘inspect element’ or ‘view page source.’

Step 3: Know the Data You Wish to Gather

If you’re analyzing reviews on Amazon, you must recognize where these are situated in the backend code. Generally, browsers highlight designated content on the front end with its matching code in the backend. Your goal is to recognize the one-of-a-kind tags that cover the associated content.

Step 4: Program the required code

When the perfect nest tags are identified, they need to be implemented into your chosen web scraping software. This informs the bot where to look and what should be grabbed. It is generally done with Python libraries, which do a part of the strenuous work.

You must tell precisely what data types you want the scraper to go through and store. For example, if you are in search of book reviews, you would need details such as the book title, author name, and rating.

Step 5: Activating the Code

As soon as you’ve written the code, the following action is to execute it. Now it’s time to wait anxiously! During this phase, the web scraper requests access to the website, extracts the data, and arranges it (as laid out in the prior step).

Step 6: Saving the Information

After extracting, analyzing, and gathering the data, it must then be put away. You can teach your algorithm to do so by incorporating extra lines into your codes. The option you opt for is dependent on you but as noted, Excel formats are typically most frequently used.

Additionally, your code can be run through a Python Regex module (abbreviated for ‘regular expressions’) to draw out a cleaner collection of data that can easily be comprehended.

Strategies to Tackle the Dark Side of Data Scraping

Even though there are plenty of practical benefits that come with data scrapping, it is also misused by only a few as mentioned earlier.

The most common illegitimate use of scraping data is email harvesting: gathering details from websites, social media, and catalogs to discover individuals’ email addresses, which then get sold to spammers or deceivers.

In some places, the utilization of automated methods such as data scraping for the purpose of collecting email addresses for commercial gain may be considered illegal, and it is commonly regarded as an undesirable marketing strategy all over the world.

A lot of web users have adopted strategies to reduce the probability of their email address being grabbed by email harvesters, for instance:

  • Address munging: Address munging involves restructuring the design of your email when published openly, for example, typing ‘patrickgmail.com’ as opposed to [email protected]. This is a straightforward yet partially unreliable method of defending your email address on social media – certain gatherers will search for numerous altered versions together with emails expressed in the accepted template, so it’s not firmly secure.
  • Contact forms: A contact form should be utilized instead of displaying email address(es) on your website.
  • Images: If the email address on your website is stated as an image, then it will be out of reach for those utilizing email harvesting.

data scraping

What Kind of Web Scrapers Are There?

Web scrapers can change significantly on a case-by-case basis. To make it easier, we will separate some of these components into 4 groups. It should be noted that there are other complexities when evaluating web scrapers.

  • self-built or pre-built
  • browser extension vs software
  • user interface
  • cloud vs local

1. Self-built or Pre-built

Much like constructing a website from the ground up, anyone has the ability to build their very own web scraper. Nevertheless, constructing one is not an easy affair; you’d need some relatively advanced skills in programming.

The tougher it is for you to incorporate desired features into your scraper, the more experienced programming knowledge you’d require.

On the flip side, there are many pre-made web scrapers that you can download and set into action instantly. Some of them even come equipped with sophisticated options such as scrape planning, exports in Google Sheets or JSON formats, and further.

2. Browser extension vs Software

Generally, web scraping approaches have two forms: browser extensions and computer software. Add-on programs that you can install on web browsers such as Google Chrome or Firefox are called browser extensions.

These commonly include themes, ad blockers, messaging extensions, and others.

Browser extension-based approaches are easier to use since they integrate directly with your browsers. However, most of their capacities are restricted by the limitations of the browser environment.

On the other hand, you can download and install actual web scrapers on your device that give you access to a broader range of advanced functions not hindered by what the browser can or cannot do.

3. User Interface

There can be a significant variation between the user interfaces of web scrapers. Some might include just a command line and limited UI, which could be considered nonsensical or perplexing by users.

Others, however, possess an abundant UI that renders the website fully functional, so individuals merely need to press the data they want to scrape. Web scrapers of this type are generally easier for people with minimal technical expertise to use.

Furthermore, some scraping tools may integrate assistance hints and recommendations into their UI, making sure that users comprehend each feature provided by the program.

4. Cloud vs Local

Where exactly does your web scraper conduct its operations? Local web scrapers function on your computer while drawing on its Internet access and resources.

If the web scraper calls for extensive CPU or RAM usage, that could make your computer extremely slow when it runs the scrape. Overlong scraping tasks can leave your device out of order for countless hours.

Moreover, if the scraper is programmed to handle a large number of URLs (like product pages), it can have an effect on your ISP’s data quotas.

Cloud-based web scrapers work off-site, using a server regularly supplied by the company behind them. This frees up resources from your computer while the scraper seizes data.

You’re then informed once the scrape is accomplished and ready for export, leaving you free to manage different jobs. This allows for quick integration of advanced amenities like IP rotation, which will prevent the blocking of major sites due to the scraper’s activities.


Thus, whether or not you plan to utilize data scraping in your assignments, it’s advisable to gain knowledge on the topic, as it will become increasingly significant in the upcoming years.

Now there is data scraping AI available that can utilize machine learning to keep developing its skill at recognizing inputs that only people have traditionally been able to scrutinize, for example, pictures.

Tremendous enhancements with regard to data scraping through images and videos will have a big effect on digital marketers.

As image scraping gets more thorough, we’ll be able to recognize far more regarding online photos once they’ve been displayed to us, and this will help us achieve various things productively, just as text-based data scraping does.

Hopefully, this article has helped you learn all about data scraping and how to use it for your needs.

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