Information and intelligence are arguably the most valuable resources an individual or business can have. Data, however, lies at the beginning of the process to obtain both, and collecting it has become just as important as the insights it hides. That is why data scraping continues to grow in importance as data-driven approaches and strategies become the order of the day.

That said, what is data scraping? In this article, we look at the concept of data scraping, its legality, and how it can supercharge some aspects of business and personal lives. Read on and enhance your capacity to collect and harness the power of data. 

What Is Data Scraping?

The term data scraping refers to any automated data collection process done online. Therefore, it is not a practice restricted to any single industry or business. In fact, nearly all endeavours with available data sources online can benefit from the effectiveness of data scraping.  

In many ways, it’s similar to having a digital assistant that collects data. Using preset rules, the data scraping tool sifts through content on the website, identifies that which you have indicated as necessary, and collects them in a predetermined order. When done right, it is a more efficient and accurate data collection method than traditional means. 

The suitability of data scraping and the advantages it confers on data collection exercises stem from its automation. Rather than physically copying, pasting, reading, and understanding, you can set a specialised scraper to sift through and collect necessary information off a page. Consequently, it frees up additional time for more creative tasks. 

For instance, you are a sports analyst looking to collect publicly available data for some professional analysis. You can go through the hassle of loading endless pages, across multiple athletes and categories and painstaking collecting necessary data points manually. Alternatively, you can configure a data scraper to identify the data you want, collect it in an organised form, and export it in a form that’s nearly ready for analysis. 

The best part about this tool is there are many types of data scraping and data scrapers. In other words, the configuration of the tool doesn’t always have to start from a generic off-course standpoint. You can purchase specialised scrapers for your data scraping and make only a few little tweaks to its functionality before it’s ready for use. 

Types of Data Scraping

The name of the tool might not always scream scraper. However, any tool that allows users to collect and download web content efficiently for consumption and analysis is a scraper. The point is you collect the data in a quick semi-automated process. With that definition in hand, it is easy to identify many different instances of data scraping.

All of the types of data scraping utilise the same element of semi-automated or automated collection. However, the process and tool will aim for specialised sources and forms of information. Let’s explore some of these types of data scraping and how they prove useful in the field. 

According to the type of content that a scraper seeks to obtain, we can have:

1. Text scraping. Most data scraping tools are not equipped to collect non-text data. As such, the understanding of the concept of data scraping is often limited to the collection of written text. An example of text scraping is the use of a generic scraper to collect and export numeric values to a spreadsheet for analysis.    
2. Email address scraping. Imagine you are a freelancer building an email list of potential clients. You probably need to collect emails on pages of potential clients and related niches. Email address scraping can help automate these tasks, making them less onerous, and allowing you to instead work on the task of convincing the wonder of collected emails that you are the professional they need.
3. Image scraping. As the name implies, image scraping is the process of automatically identifying and collecting images from a source. The collection could be based on various factors and criteria, including keywords, sizes, resolution, types, etc. A relevant scraping tool will crawl through the source and collect images that fit your criteria. This form of data scraping is particularly important in the collection of images for research, training machine learning models, etc. 
4. Video scraping. Video scraping involves the use of specialised tools to crawl through a source, identify video content, and collate it for bulk collection. In reality, many video scraping tools may refer to their activity as video downloading or any variant of the term. As such, specialised video scrapers may be referred to as video downloaders. Regardless of their names, these tools have the unique ability to help facilitate bulk downloads quickly. In addition, some of them might help download content that is not otherwise regarded as downloadable. 
5. Social media scraping. Social media is a treasure trove of useful business and brand-related information. Social media data scraping involves collecting interactions under a post, sentiments about a brand or product, etc. for the sake of research or brand monitoring. For instance, a confectionery business can use social media sentiments about its products to assess its performance, and possible areas of improvement. 

There are many other types of data scraping based on the type of content they aim to collect. Another type of data scraping categorization focuses on the source of the data. Based on this categorization, here are the three types of data scraping:

1. Report mining. In this type of data scraping, the source of the data is structured. So the process involves collecting these organised data for further analysis. The structured data required for report mining may come from enterprise software or recognized professional data sources.  This type of data scraping falls under the purview of traditional data collection for research in businesses, sports, and the like. 
2. Screen scraping. Screen scraping refers to the collection of data from unstructured sources or sites where APIs are not available. It also includes the automated collection of data from sources that might not have code to parse or a manipulatable format. An example of screen scraping is trying to pull data and content from a legacy machine onto a modern machine. The scraping will involve collecting the data in its original form, then decoding and transforming it into a form that’s understandable by the modern computer. A more relatable example is the use of machine learning and image-recognition algorithms to extract data from a screenshot and produce it in a data form that can be understood by another program. For instance, the use of the Google Docs ‘autofill’ function to scan text from an image or hard copy material and incorporate it into a digital document.
3. Web scraping. Web scraping is the most recognizable form of data scraping. This is the scraping of data off websites. In this type of data scraping, users use specialised tools and scripts to isolate data types on websites for automatic collection and collation. The type of scraping involves parsing HTML and XML code, interacting with web pages,and  communicating with APIs to collect data and collate them in a structured format. A popular example of web scraping is the collection of publicly available online data for analysis by researchers and businesses. Specific use cases of web scraping include:
   • Mass data collection, including sports stats, prices on online marketplaces, and stock price information. 
   • Competitor analysis, where businesses monitor and collect competitor prices, customer reviews, and product descriptions. 
   • Market research, where businesses use this form of scraping to collect data (like government census data and social media sentiments) to assess opportunities, pain points, brand image, and other relevant information. 
   • Content aggregation, such as in the scraping of RSS feeds to automatically create comprehensive databases of information for consumption or use. 

Our final categorization views data scraping from the lens of the actual method employed in the process. Based on this, there are:

1. HTML parsing. Here, the scraper or script extracts the data from the HTML code of the website or web pages. In this case, the scraper navigates the website, identifies the required data types, extracts them, and downloads them into a structured form. 
2. API scraping. In API scraping, the scraping script or tool interacts with the website via an Application Programming Interface. Most websites don’t offer this interface, but those that do essentially provide a more efficient means for users to indicate and retrieve the structured data they want.

Is Web Scraping Legal?

The legality or illegality of web scraping is a complex issue to unpack. In the simplest understanding of the process, web scraping is not illegal. It is simply a process that allows web users to collect publicly available content automatically and quickly. 

When broken down, it can be illegal depending on the type of content being scraped, the stance of the website publishing the content, and how the scraping occurs. Therefore, to keep your scraping legal, you need to know what Ts to cross and Is to dot before you begin. 

1. Terms of service of the website. This should be your first stop before scraping. Most websites detail how they expect their data to be consumed and used in their terms of service.  There might be specific bans on scraping activity. Popularly scraped websites can suffer numerous requests from scraping bots, which in turn slow down their servers. Their most familiar punishment is to temporarily or permanently ban the IP addresses of defaulters. However, a user can also find themselves at the end of legal action. Summary: read the terms of service first.
2. Copyright. Depending on where you are scraping your content and the form it appears in, it might be subject to copyright protection. In such a case, scraping without appropriate permissions may be construed as copyright infringement. However, the very nature of the content published online makes this a tricky territory, hence the development of concepts like ‘fair use’. Read up on all of this, determine whether the content you are scraping is protected, and identify how to keep your scraping and use of scraped data legal.
3. Potential Fraud. Depending on the locality you find yourself and that of the servers hosting the content to be scraped, the process may be illegal. Similarly, circumventing security measures to prevent accessing and scraping certain data may be tantamount to defrauding the content creators and web admins. Check your local content use and distribution laws to be sure of where you stand. 
4. Ethics. While scraping might not be illegal sometimes, the ethics of obtaining content and how you obtain it are important considerations. As such, users are encouraged to practise legal data scraping in a way that doesn’t negatively impact the functioning of the website or the profitability of content creators and aggregators.  

In summary, if you want your data scraping to stay above caution, consider asking for permissions where necessary, scraping with ethical considerations in mind, and using the content obtained in a legal, responsible manner. 

What is Data Scraping Used for

At this point, you know what data scraping is and how to categorise the types of data scraping. We’ve even explained how to assess the legality of data scraping and stay responsible while doing it. Here, we explore some of the specific ways that people, businesses, and other entities employ data scraping.

Market Research

Regardless of field, industry, or the age of a business, data scraping can help market research. The reasoning is simple: there is a multitude of data available for companies to collect and leverage for their success. Data scraping allows the efficient collection of as much of this data as they can find. In other words, they are significantly more informed about their market than they would without data scraping.

Content Aggregation

People involved in media and marketing need to stay informed at all times. Data scraping allows them to comprehensively and efficiently collect everything relevant to their fields from their sources. In this way, they can create and update a content hub for their interests in a much easier manner.  

Job Search

Try as hard as you might, you cannot manually track all job postings across all relevant platforms. It’s just like you would struggle to manually monitor every interaction to a moderately successful social media post. Automated data scraping removes the manual and makes it easy for people to scrape for relevant job listings in their field.  

Social Media Monitoring

Social media is an important frontier in 21st-century brand-building and business development. As such, the chase to optimise social media use to better understand customers, their expectations, and their pain points is on. Data scraping allows brands and businesses to collect lots of relevant data, essentially allowing them to conduct unofficial surveys on consumer sentiments.

Lead Generation

In the eyes of many, this toes the line of acceptable online and web scraping behaviour. However, web scraping can allow users to collect contact information on potential clients and customers for lead generation purposes. Such information may include email addresses, social media profiles, etc.

Financial Market Analysis

Knowledge and information are key ingredients necessary for success in the financial markets. A drop in the mining productivity of a critical material somewhere in Africa could tank the production of a factory in China, and crash the market price of your favourite tech company in the United States. Data scraping can help you access the relevant information in real time, and thus inform your positioning in the market.

Using Proxy for Data Scraping

Data scraping allows people and organisations to efficiently collect data from sources. However, when it comes to web scraping, the scraping bot or tool might not be enough if you don’t have access to the website. Not to worry, this is where web proxies come in.

A proxy is a web tool that reroutes your traffic through a proxy server in another location. As a result, a user’s web requests appear to originate from a different location to web admins and the destination servers.

From the perspective of data scraping, proxies provide access to otherwise inaccessible. There are a couple of situations where web sources might be inaccessible for data collection and the use of proxies becomes necessary. The most popular of these are:

1. Blocked IPs. In the event that a user violates the terms of service of a website or platform, they may block said user. The favoured blocking mechanism identifies activity by IP address and makes connections from the IP address impossible. In this case, by rerouting requests and traffic through a proxy server, proxies fool the destination servers and restore access.
2. Rate limits. Some websites may employ rate limits to slow down activity from overactive IP addresses. As such, they could slow data scraping and collection to a crawl. A web proxy allows users to change IP addresses regularly, thus avoiding the rate limits.

We have established that proxies are useful for data scraping. However, just like humans, all proxies are not created equal. Some are faster, better, and more reputable than others. Here are a number of tips to help you pick the right proxy for your data scraping:

1. Don’t go for free proxies. A free proxy lacks the infrastructure of a paid one. Furthermore, they are unlikely to be as well-maintained. Their reputations are also difficult to assess. In other words, they can be inefficient, poorly updated, and lacking security. Commit to paying for quality proxies instead and use customer reviews to select a suitable one.
2. Pick the right type of proxy. There are many different types of proxies. We have residential proxies, static proxies, rotating proxies, datacenter proxies, mobile proxies and so much more. Not all are suitable for data scraping. Our recommendation for data scraping is to purchase rotating residential proxies that guarantee elite anonymity and automated IP address changes.
3. Consider speed and performance. As part of your research on the right proxy server for data scraping, factor in speed and performance. Connection speeds, download speeds, latency, and downtimes all have a role to play in the efficiency and performance of a web proxy for data scraping.
4. Make sure they have great customer support. Like all web tools, a proxy can stump you regularly if you lack the technical expertise to manage it. As such, ensure that your chosen proxy provider has a strong customer support structure to supply help when or if you need it.

Frequently asked questions

Can data scraping be done manually?

Yes, you can collect all forms of data manually. However, doing so without automation loses the essence, efficiency, and speed of data scraping.

Is data scraping suitable for small businesses?

Yes, data scraping is suitable for small businesses. Regardless of the size of a business, data can provide insight that facilitates its development. Whether it’s scraping data off the visitors of your online store or collecting data for market research from competitors and social media, the ability to generate important insights is what determines suitability.  

How can I get started with data scraping?

To get started with data scraping, you simply need to purchase a data scraping tool or a scraping API. Some proxy providers like Proxy Cheap offer these tools alongside their proxies. 

What is the role of proxies in data scraping?

The role of proxies in data scraping is to help their users access websites where they are restricted, hide their IP addresses when they are blocked, and stay anonymous as they scrape large volumes of data.