So many professionals and companies are using web scraping to keep track of the latest updates in their respective fields.
But, the majority of them don’t really grasp the scale that this technique can provide.
The thing is that the growth of technology pushes everything to new heights.
Any technology we used years ago, we can still use today, but the actual application and the scale are much bigger than before.
Think about it; the internet started in 1983 as a way for researchers to share information.
Now it is so much different than the initial idea behind it.
Thus, you can already have an idea about the complexity of web scraping as a concept.
As the digital world expands, so does the potential and amounts of data to scrape from it.
Let’s begin to uncover the subject by first defining its meaning.
What Is Web Scraping?
Web scraping is the process of extracting data from websites.
Information is collected using automated software applications and then structured into a usable form.
Web scraping is a powerful technique for extracting data from websites.
Nevertheless, the whole process is extensive and has its own sub-meaning.
But before we dive into the details, some things need to be clarified.
Web scraping is known to many by a variety of different names, including:
- Data Scraping
- Data Extraction
- Data Crawling
- Web Crawling
- Web Harvesting
- Screen Scraping
There are slight differences between those, but generally, the idea behind them is the same, to extract the data from the internet in an automated way.
In this regard, automation is the key. It has been the driving force behind a lot of today’s cutting-edge technology.
Starting from 1934, when IBM manufactured and sold document readers and sorters for financial institutions.
It didn’t take long for everyone to realize that it’s not just for the government or large corporations, but SMEs can also leverage it.
Ever since, the passion for technology has eased our lives in countless ways.
Ultimately, its purpose is to simplify life by automating various tasks – and web scraping is no exception.
Let’s dive into the process and see everything in depth.
Web Scraping Process: In-depth Guide
The web scraping process implies using software to identify, extract, and process data from websites.
The output can be biased if you make any mistake in the process or miss the step.
It requires a structured approach.
That is why it is crucial to follow everything step by step and make sure you can validate the data.
Everything starts by choosing a website to scrape.
Once you are ready to move forward, you can start by locating the necessary fields.
Sometimes the objective is extracting only prices, maybe from product pages.
The aim can also be customer reviews, provided maybe within the last year.
If you are going after the leads, any kind of contact information might be useful for you.
Take your time to think about the value that you are looking for and make sure you are not missing anything.
Be careful not to overwhelm yourself by simply thinking about grabbing it all.
Excessive data can be as disruptive as not having enough.
Process in Details
If you have the proper resources and are not intimidated by a large-scale project, then starting out on your own is entirely doable.
But if you are wondering what data extraction at scale looks like or what it involves from a technical standpoint, here is the list for you:
- Identifying the target website.
- Analyzing website’s technology.
- Analyzing HTML content and elements of web pages.
- Analyzing API calls used between the website and the browser.
- Locating necessary fields for the extraction.
- Finding an exact location or an API call on the website for those fields.
- Creating validation protocol for extracting the data.
- Creating quality assurance measures.
- Developing a crawler that gathers all data containing selected data fields.
- Developing a scraper that extracts specific fields from the crawled data.
- Developing a parser that formats selected fields from the scraped data.
- Developing a data cleaning algorithm that cleanses parsed data.
- Developing an algorithm for creating the final output structure.
- Finally, start analyzing the data.
The process above is known as web scraping.
Now you understand that it’s not as straightforward as it appears.
You may think otherwise, but the truth of the matter is undeniable: it requires a lot more effort to complete than most would anticipate.
To gain further insight into this matter, let’s investigate its roots.
Origins of Web Scraping
Origins of web scraping date back to 1989 when British scientist Tim Berners-Lee created the World Wide Web.
The idea was to have a platform for scientists in universities and institutes worldwide.
The aim was to facilitate faster communication between them.
So his’s original goal was to create an information management system.
But in the end, he managed to create a whole world and a society connected by technology.
The very beginnings of HTTP protocols just allowing us to fetch the resources are the basis of web scraping.
Data on the web gives us the option to learn everything much faster.
We use the web to communicate, share ideas and express ourselves.
The sheer number of possibilities is enormous, and only the ones with the data can predict what the future may hold.
Well, that’s certainly a bit of an exaggeration, of course, no one knows what lies ahead, but think about it.
With enough data, we have the potential to forecast it.
Combining it with statistical algorithms and machine learning techniques can definitely help to identify the likelihood of future outcomes.
To ensure maximal accuracy, all efforts must be founded on historical data.
For that purpose, web scrapers must be developed.
So let’s dive into web scraper development.
How Can You Create Web Scraper?
Programming languages use specialized libraries or frameworks.
For example, the most known ones in Python are Requests and Selenium.
One helps with directly requesting the information from the website’s API endpoint.
And another helps to automate the browser to go through web pages just like a regular user.
Even though you can create scrapers in various programming languages, Python is the most popular.
It enables faster coding, which is very helpful in keeping up with website changes.
Usually, software developers create a custom web scraper from scratch.
So, it’s better to have a plan first.
You should decide on the actual need and the scale.
That helps to define which approach to use.
Once everything is clear, it’s time to calculate the resources.
Well, the most obvious one is Proxies.
You may need to rotate IP addresses before accessing the web page.
It’s a common practice that a single IP accessing website too fast too frequently may lead to a block.
Sometimes, Captchas are standing in between the data and the scraper.
And if it’s integrated deeply, it might be a problem.
Well, not a problem, but it can definitely slow things down.
In the worst-case scenario, you may need to create separate software to solve it.
Once you take all of the necessary steps, a dedicated server is absolutely crucial, especially when dealing with large-scale projects.
Furthermore, you should consider potential modifications to the website’s composition when scraping.
It’s a bit tricky to take into account everything, but you should definitely do as much as you can before the start.
Only after that, you can start creating the scraping software, which queries a web server of the target website.
A website serves data in the HTML or JSON format and other files that comprise web pages.
Sometimes, the process may involve downloading several web pages or the entire site.
The downloaded content may include just the text from the pages, HTML as a whole, or both the HTML and images from each page.
Once scraping software structures all that mess, it parses the data to extract necessary fields in the desired output.
In some cases, you may need to do a manual overview or use built-in opt-out scripts to analyze websites.
Ultimately, the rewards of these efforts could be priceless.
After all, it’s a great way to help businesses make data-driven decisions.
Let’s see how it looks in practice.
Web Scraping In Practice
Web scraping, in practice, is very similar to copying and pasting data from a website.
Except the automated nature allows it to be used on a much larger scale.
Web scraping is an automated process that can lead to dramatic reductions in workloads.
Meaning completing months of tedious work in a matter of moments.
Well, it might not be entirely accurate.
After all, time to build a customs scraper takes time as well, but once it’s built perfectly, extracting the output works relatively fast.
With its ability to rapidly and precisely collect data from a vast number of websites, web scraping can give you access to an enormous amount of legally available information.
In practice, web scraping encompasses various programming techniques and technologies, including data analysis and information security.
Sometimes to scrape a website, the web scraper has to act as if it was human.
So while some non-techies may find it humorous, sometimes all these complex technologies are just used to replicate human behavior.
Undoubtedly, they would be amused to discover that the majority of issues originate from precisely this point.
That code sends a request to the server.
The server then processes this request and responds to the client or browser.
It can be challenging for automated programs like web scrapers to know when to send requests to the server and when they should not.
That’s why it is always best to dedicate a software developer to that job.
Unless the task is so small, it can be done using automated online tools.
One way or another, building a scraper isn’t as much of a problem as maintaining it.
The amount of maintenance required really depends on how well you build it.
But to put it in perspective, websites change, and they do it rather frequently.
Future-proofing everything is kind of an impossible challenge.
Nevertheless, it’s more important how quickly you can find and fix the problem rather than how frequently it occurs.
On the other hand, having a scraper that needs support every minute or so wouldn’t be efficient.
That’s why it requires an immense amount of work to develop something really reliable.
However, when you manage to do so, the software can work for years without having to change it.
Yet, as a rule of thumb, it’s always a good idea to refine it at least every month or so.
Why is Web Scraping Needed?
To define the need for web scraping, we might start by discussing who needs the data at all.
Web scraping is needed because it can supply organizations and individuals with the latest and most important data insights.
Every single entrepreneur and legal entity in today’s world is included here.
It might sound a bit far-fetched.
But is this not the world we live in right now!?
Isn’t the purpose of the whole internet to receive data in a more modern manner?
When competing with each other, is there anything more valuable than having access to even more information and raw data?
Companies strive to conquer digital obstacles in order to reach their full potential.
Data and Digital Transformation
Furthermore, the process of whole digital transformation is all about utilizing and examining data, both external and internal sources, to unlock better outcomes.
Every success story of recent times has relied on digitalization as a way to maximize efficiency and thrive.
So, even though some industries need web scraping more than others, every business requires it at a certain level.
That’s the exciting fact; it can integrate into any workflow.
Keep in mind that the purpose behind web scraping isn’t just extracting the data but using this data to enhance any current process.
That can be a sales channel, a method of analyzing customers, reviewing feedback, monitoring prices, and so much more.
To boil it down, the need for useful information is precisely what leads to forming web scraping as an independent type of work.
An automated technique combining research and analysis gives an ever-evolving approach to seeing the world in data colors.
How Does Web Scraping Work?
Web scraping works by targeting and extracting public data sets available on the internet.
It enables viewing databases spanning thousands or even millions of pages, sometimes even within minutes.
But on the other hand, web scrapers are excellent at gathering and processing large amounts of data sets simultaneously.
It gives you structured web data from any public website.
Let’s suppose you have an online shop and want to keep track of your competitor’s prices.
You could visit your competitor’s website every day to compare each product’s price with your own.
But this would take up a lot of time, especially if you sell thousands of products or need to check price changes frequently.
What if you are a retailer looking for the best provider of goods?
You will find one but definitely miss the opportunity of available discounts or market condition changes.
It’s exactly the same for large enterprises that constantly monitor procurement data using web scraping for business intelligence.
To give you an idea about the frequency of usage, here is the number:
Approximately 21% of current e-commerce traffic comes from pricing web scrapers.
Web Scraping Algorithms
As we discussed earlier, web scraping is a complex process.
It requires a chain of interdependent algorithms working together to extract target information from web pages.
Once the process is started and the software downloads web data, everything becomes more interesting.
As we know, it then uses the fetched web page’s content to understand what further information it should look for.
After extracting relevant information and structuring everything for the final output, we are ready to start mining specific data fields.
Here is the thing, web scraping involves accessing a website’s data directly from its HTML code or API calls.
And that makes it vulnerable to changes in the pages or data structure.
For this reason, it is important to have a robust system in place that can keep up with changes.
Software built this way can adapt to changes and automatically incorporate new extraction methods.
Most Commonly Used Algorithms in Web Scraping:
Regular Expressions: Regular expressions are widely used in web scraping to extract structured data from web pages. These patterns match specific strings (texts) based on a defined set of rules, making them a highly versatile tool for searching and extracting information from blocks of text. You can use them to locate email addresses, phone numbers, and recognizable structured data types.
XPath: XPath is a language for navigating XML documents, including HTML pages. It allows users to select specific elements or attributes from an HTML page based on their location and hierarchy in the document tree. With XPath, you can easily extract data from pages that have complex or nested structures, which can be difficult to navigate using other algorithms.
Less Popular Algorithms
Tree Matching Algorithms: You can use these algorithms to compare the structure of two HTML pages and identify similarities and differences. Tree matching algorithms can help you to detect changes in a website’s structure, such as the addition or removal of elements. By comparing the layout of different pages, these algorithms can locate the position of particular elements such as headings, links, and tables. This algorithm can help you in automating the process of data extraction and analysis from multiple web pages simultaneously.
Ruzzo-Tompa Algorithm: The algorithm works by first tokenizing the web page, which involves breaking it down into individual words or tokens to assign a score based on predefined classifiers to evaluate the relevance of each block based on location on the page, its proximity to other tokens, and the surrounding context. You can then extract the resulting subsequences of tokens from the web page and use them for further analysis.
Natural Language Processing (NLP): You can use NLP algorithms to analyze the content of web pages and extract meaningful insights. They can help you detect the sentiment of a web page and analyze it for topics, phrases, or keywords. For example, by using NLP algorithms, you can identify the sentiment of customer reviews to classify given texts and define positivity levels.
Machine Learning: Machine learning algorithms are often used in web scraping to automate the process of data extraction. You can train these algorithms to recognize specific types of content on web pages to future-proof the process and extract everything automatically. Moreover, machine learning results can also help you to refine and validate the efficiency of scraping algorithms.
By combining these methods, web scraping software can extract and analyze huge amounts of internet data.
Consequently, with the right algorithms and tools, web scraping can become an invaluable asset for gathering material insights.
Web Scraping as a Service
Web scraping as a service (WSaaS) is a type of service that provides a scalable and efficient way to extract data from websites.
It is a popular option for businesses and individuals who need to collect large amounts of data from the internet.
It’s a great solution if you do not have the time, expertise, or resources to develop your own web scraping software.
Web scraping as a service is no different from regular scraping except that it’s completely customized to a specific task.
Here’s how it works – you describe what data you want and dictate how frequently you would like to have it.
You can also add something like formatting preferences to make sure it’s structured as you expect.
Might as well prefer some flexible delivery options like an API or cloud storage.
Once everything is clear, you start receiving the data in a desirable manner.
Things To Consider
However, there are still some things you need to think about.
And those things are how you plan to utilize, store, and use the data.
It will help you to set the bar at an appropriate level.
Since it’s absolutely crucial to aim scraping the data within a reasonable scale.
This way, you can guarantee that you are using everything effectively.
Now you can imagine scraping prices for a thousand products every second.
Well, sure, it sounds amazing; that’s at least 86.4 million rows of data per day.
Will scraping, storing, and analyzing that amount of information worth the value it provides?
Reducing the frequency and increasing the target market could be more beneficial.
The right choice is always case-specific.
But it’s vital to concentrate more on utility rather than ability.
And take into account that if something’s manually accessible, then it can be automated as well.
Web Scraping Technology in a Nutshell
You can extract just about everything today; having said that, web scraping is a new term.
I believe there is still a lot to learn in this field, and it’s not only about technology.
As the legal side of things improves, in a way, it affects the scraping industry as well.
When Ninth Circuit confirmed that data extraction from publicly available sources does not violate America’s Computer Fraud and Abuse Act (CFAA), things started changing.
You may be wondering what it has to do with the technology, but here is the deal;
Certain companies have been concerned that they cannot protect their data within the confines of the law.
That led to increasing technological defenses and mandatory improvements of anti-bot systems on websites.
What does that mean for web scraping?
Well, it became tougher, which basically led to more discoveries.
As a result, it accelerated advancements in scraping technologies.
After all, web scraping algorithms are strictly dependent on how the developer stores the data on the websites.
What roadblocks does software have to go through to extract what’s required?
What would be the solution to fight against multiple defense mechanisms?
That little back and forth between scraping and web developers tightened both ends.
While some may find it amusing, this has led to the development of improved website protection systems and more efficient web scraping solutions to bypass them.
How To Stop Scrapers?
If you are wondering if it is possible to deny the automated scraping of your data completely, the answer is No.
It is critical to understand that unless the website’s information is stored behind a login, there will never be an absolute way of preventing scraping.
If the data is behind the authorization and requires software to log in, even though it’s possible, it’s never a good idea.
Usually, websites hiding data behind the authorization are either profiting from their information or protecting sensitive content.
Scraping private information is not only illegal but also unethical.
This truth goes beyond any legal implications; it’s simply not the right thing to do.
If you are in a business and want to overcome your competitors, there is always a way and a completely legal one.
No matter what, it’s not worth the risk;
If you’re tempted to buy something that may be counterfeit, remember—tomorrow, you could end up becoming a victim.
Nevertheless, the more data we have, the harder it gets to choose the right path.
You shouldn’t defy your intuition, but it wouldn’t hurt to have some data to back it up too.
Maybe aim to make a decision based on the facts and even make predictions.
I might be wrong, but who doesn’t want to know more about their industry and competitors?
So, what’s the hold-up?
Let’s dive into the potential and benefits scraping can provide.
Web Scraping Potentials and Benefits
Web scraping has the potential to extract valuable data and insights from websites.
That provides multiple benefits, such as competitive intelligence, market research, and lead generation.
It’s like having a cheat sheet about the industry and helping you to isolate the competition and find a way around it.
Trying to automate the process of data acquisition helps to stay one step ahead by using advantages based on factual data.
Even so, the full potential and benefit of web scraping are yet to be seen.
Businesses constantly boost day-to-day operations, but they don’t have to do it blindly.
Instead, they can let the data lead the way.
With the immense ocean of information that is available to us, we often overlook just how much data is at our fingertips.
The Internet holds at least thousands of Petabytes of data, that’s billions of Gigabytes.
If you still need to be impressed, that’s about the equivalent of 300 million books, or 500 times all the words spoken by humans since we first appeared on Earth.
Therefore, it’s not unexpected that more companies are switching their attention to digital rivalry, making the market even more challenging.
With web scraping, it’s possible to automate most of the data needs that can improve decision-making and drive innovation.
It can help to utilize ways of finding new customers, increasing customer retention, improving customer service, predicting sales trends, and more.
So it might be time to simply feed your business with live data insights.
Web Scraping Popular Uses
Web scraping is becoming increasingly popular as it enables extracting and analyzing large amounts of data from websites.
The beauty of it is in the diversity of information you can extract.
Here is an example of 10 types of information you can easily extract using scraping:
- Product information – from e-commerce websites.
- Business contact information – from directories or social media platforms.
- News articles, headlines, and other content – from news websites and blogs.
- Job postings and candidate resumes – from job boards and recruitment websites.
- Real estate listings – from real estate websites.
- Social media data – from various social platforms.
- Financial data – from financial websites and news sources.
- Geospatial data – from government websites and databases.
- Travel data – from travel booking websites.
- Research data – from academic databases and research websites.
The list of potential applications for this technology is seemingly infinite.
The only limitation is your own creativity and the industry you are operating.
Take into account that data itself holds no value unless the person analyzing it knows what to look at.
If you can show off your technical smarts and industry know-how, the potential for achieving wild success is virtually limitless.
Nothing can come close to the industry expert armed with factual insights, ready to make a change.
So, with the right approach, you can easily reap the benefits of web scraping technology.
Let’s review a couple of detailed examples of web scraping use cases:
Web Indexing: It is estimated that 70% of all data originates from websites. Search engines use web crawling to find fresh content and index pages. Webmasters analyze and check the health of their sites.
Data Mining: That can be a unique way to test a business hypothesis by explicitly mining automatically extracted information. That may even help to launch a trial of your product or service on a section of the market.
Data Analysis: This is particularly valuable in a variety of industries, the most evident being finances. Scrapers allow you to monitor real-time stock quotes and share prices, as well as economic indicators.
IT & Digital: Web scrapers are used to monitor news, reviews, and listings about products. Technology companies can leverage it to gain an edge over the competition, while agencies can monitor emerging IT trends.
Research & Development: Web scraping can collect data accurately, which is especially useful for scientific organizations. It can reduce the need for expensive procedures allowing access to a vast range of information without expending large sums.
SEO: Almost all SEO tools have integrated crawler/scraper that allows webmasters to analyze the pages and the whole content. Extracted information helps to inspect ranking factors and test their validity.
Social Media: Web scrapers can be useful for monitoring popular trends across social media sites. For businesses wishing to expand their digital reach, this is a highly beneficial solution.
Is That It All?
Despite providing numerous illustrations, it still feels like something is missing.
Web scraping is an essential tool for virtually any industry, providing a variety of uses.
It looks like data and automation are connector nodes, especially between physical and digital words.
As big data and AI become increasingly ingrained into our daily lives, companies are on the hunt for valuable resources that can help them succeed.
Whether your goal is personal or commercial, it seems like there is always room for a little more data.
Where to Start? Conclusion or a Feature
As technological advances continue to shape the market, businesses are experiencing shifts.
Accelerated progress changes the needs and demands of companies, and they are becoming increasingly volatile.
Upon reviewing the fundamentals of web scraping, it became apparent that this is a straightforward yet robust technique for countless applications.
Consequently, there is nothing in the world holding you back from learning more.
Hence, it is vital to recognize the impact of missing opportunities.
We are Datamam, and our mission is to help companies make decisions faster by effectively extracting, organizing, and analyzing data at scale.
We understand how data can create real change; and, therefore, aim to build a world inspired by data.