Despite the existence of highly valuable data, one study found that organizations ignore up to 43% of available data. And even worse, of that collected data, only 57% is actually used. But why does this happen?
Without a way to extract data, including unstructured ones, companies can't harness the full potential of information and make good decisions.
So, for example, working with a good data set allows the machine learning model to run smoothly and correctly, the marketing team can create successful campaigns, and the financial firm can make the right investment decision. And that's just a small part of what can be done with data extraction. Applying this data collection method will bring countless benefits to your processes, regardless of your industry. But first things first.
What Is Data Extraction?
Data extraction is the process of obtaining data from various sources for processing, storage, or analysis elsewhere. Web data extraction involves collecting data from multiple sources such as web pages, emails, flat files, documents, Portable Document Format (PDF), scanned text, etc., and is often used when talking about data extraction.
Typically, data is first analyzed and then reviewed to retrieve any necessary information from the sources. The sources from which data is extracted can be structured or unstructured.
The importance of data extraction cannot be ignored because it is an integral part of the data processing workflow. It turns raw data into strategically important information that can have a real impact on a company's bottom line.
Two Types of Data Extraction
Web data extraction usually occurs for one of the following reasons:
- To archive data for safe and long-term storage
- For use in a new context
- Prepare the data for further analysis is usually the most common reason
Extracted data is usually stored in a "data warehouse" for future use. The tools for transferring and transforming data are Extract, Transform, and Load (ETL). Extracting data and determining its value is the most difficult task in ETL. In conducting this complex process, data engineers make decisions that relate to:
- extraction method;
- how to clean and transform the data for future use.
When it comes to extraction methods, there are two options - logical and physical.
Logical is divided into full and incremental data extraction.
- Full Extraction
In this method, data is extracted completely and directly from the source system at once. The source data is provided without any additional logical or technological information, such as the update date of the source system.
Each extraction is independent and represents a full download of the current state of the data. For example, to export a single file relating to price changes, the system completely extracts the organization's financial records, copying the entire table.
Full web data extraction is best used when you do not want to track changes that may have occurred since the last extraction and you only want access to the data. However, if you want to know what data changes are occurring in the source system, you will need a second extraction method.
- Incremental Extraction
With incremental extraction, changes are tracked in the source data since the last successful extraction, then retrieved and loaded into a new system, such as a data warehouse.
Change Data Capture (CDC) helps you determine what changes have occurred since the last retrieval while avoiding retrieving the entire dataset again.
Although many data warehouse systems don't use change capture, but rather retrieve the entire table from the source system to the stage area and compare the data to the previous version's table and identify the changed data.
Physical is divided into online and offline data extraction.
- Online Extraction
Online data extraction represents the direct transfer of data from the source system to the data warehouse. For the process to be functional, the extraction tools must connect directly either to the source system or to a transitional system with pre-configured data chambers.
- Offline Extraction
Data is not extracted directly from the source system but is structured outside of it, for example through extraction procedures. Data extraction and the stage at which the ETL process is performed also affect the definition of the extraction method.
We must remember that data extraction is an ongoing process. The repositories need to be updated as new data becomes available in the source systems.
What is Web Scraping?
Web scraping is the process of automated content and data gathering on the Internet. Typically, it uses software that simulates human web surfing to extract data. The resulting data is stored in a local file for later viewing and analysis. Individuals and entire companies use web scraping to make smarter decisions.
If you've ever copied and pasted information from a website, you've performed the same function as a web scraper, only manually. Unlike routine manual data extraction, web scraping uses automation to extract millions of data points.
Manual Web Scraping
Web scraping can be done manually. You only need to copy and paste the information into a table or document to keep track of the extracted data. This is the easiest method of web scraping, where you can check every information you collect. For example, if you only need to find a few phone numbers or addresses, manual data extraction is a good way to do it.
However, it is the slowest and most time-consuming method of web scraping with the risk of human error. And human error can cost you a lot.
Automated Web Scraping
Automated data collection is becoming increasingly popular due to its ease of use, time & cost savings, and the ability to handle giant amounts of information.
Most scraping services have a user-friendly interface that allows you to collect information without coding skills and output data to files like Google Sheets, JSON, XLSX, CSV, XML, etc. Essentially, creating a live API for any data set on the web.
Web scraping tools come in all shapes and sizes, from browser extensions to software solutions. We'll talk about that a bit later.
Also, you may have encountered not only the words web scraping, but also web crawling, data mining, and API scraping. An unfamiliar person might think that these are the same thing. But here we're going to explain to you how they differ from each other.
Web Scraping vs Data Mining
As mentioned earlier, web scraping is about collecting data from web sources and structuring it into a more usable format. Web scraping does not involve any data processing or analysis. Scraping tools and applications simply collect valuable data and extract it according to your needs, and all the information is stored in a central database for future use.
Data mining is already the process of analyzing large sets of raw data using various machine-learning technologies to identify trends and valuable information. Data mining does not involve collecting or extracting data, as web scraping is used to create datasets to be used in data mining.
Web Scraping vs API Scraping
Both of these methods allow access to data. The main difference between them lies in the operating principle.
Web scraping collects data from the required public sources on the Internet using manual or software tools. Of course, it is preferable to use software tools because they are faster, more powerful, and more convenient than the manual method.
API is an abbreviation for Application Programming Interface. Through API you can access the data of an application or operating system. It is a kind of intermediary that allows one program to access another. Namely, the API passes your request to a provider and then returns a response to you. So, the API depends on the owner of the data set. The owner can limit the number of requests one user can make, or the amount of data requested.
With APIs, data is usually retrieved only from one target website, while with web scraping, data is available from multiple sites. In addition, with web scraping, the user can access the data as long as it is not available on the website. The API allows only a specific set of data to be retrieved.
Web Scraping vs Web Crawling
If you're not a technical person, the words "web crawling" and "web scraping" might sound like they mean the same thing. In reality, they are two very different processes that are often confused with one another.
What is Web Crawling and How Does it Work?
Web crawlers are a type of software that automatically discovers and gathers information on the World Wide Web. A crawler typically starts with a list of URLs to visit, called the seed set. As the crawler visits these websites, it discovers links to other websites and adds them to its queue. The crawler continues to crawl until it has visited all the websites in its queue or until it reaches a pre-determined stop condition.
Web crawlers are usually programmed in one of two ways: depth-first or breadth-first. Depth-first crawlers start with the seed set and then crawl down the web graph, visiting each child node before moving on to the next sibling node. Breadth-first crawlers start with the seed set and then crawl across the web graph, visiting each node's siblings before moving on to its children.
There are many different web crawler tools available, both open-source and commercial. Some popular open-source web crawlers include Apache Nutch, Heritrix, and Xenu's Link Sleuth. Commercial web crawlers include Microsoft's Bingbot and Google's Googlebot.
The Difference Between Web Crawling and Web Scraping
Unlike web crawlers, which automatically follow links to find all of the content on a website, web scrapers extract only the specific data that they are programmed to look for. Web scrapers can be used to collect data such as prices, product descriptions, contact information, and more. Many companies use web scrapers to scrape data from competitors' websites so that they can keep track of their prices and product offerings. Some companies also use web scrapers to collect data from sites like Amazon and eBay to create their own product database.
Both web crawling and web scraping can be used to collect data from websites. When deciding which tool is best to use in your case, it's important to consider the type of data you need and the source of that data. If you need large amounts of data from multiple sources, a web crawler will likely be your best option. However, if you only need data from a few specific sources, a web scraper will probably suffice. In short, web crawling is best for large-scale data collection, while web scraping is more suitable for specific tasks.
Types of Web Scraping
These web scrapers are extensions plugged into your browser to automatically collect any web page you visit. The advantage is that they are easy to use and integrated directly into the browser and are good for those who want to collect small amounts of data. However, they do have limitations in their operation. For example, any advanced features that go beyond your browser cannot be run on browser-based web scrapers extensions.
Web scrapers as installed software, unlike browser extensions, have many additional features, such as rotating the IP address for more efficient data collection, gathering information from multiple web pages simultaneously, running in the background separately from the browser, displaying data in different formats, searching the database, scheduling scraping sessions, and many other functions.
Cloud and Local Scrapers
Cloud-based web scrapers run on an external server. Mostly it is provided by the company from which you buy the scraper. You do not need to install it on your computer, just set up a data plan and requirements for the collected data. Unlike browser-based extensions, cloud-based scrapers allow you to integrate advanced features and collect large amounts of data.
Local web scrapers run on your own computer, use local resources, and are great for small tasks.
You can also create your own web scraper. However, to create it, you must have an in-depth knowledge of programming. There are also pre-made web scrapers that someone else has created before you and that you can easily download and launch.
How Do Web Scrapers Work?
The goal of a web scraper is to understand the website's structure in order to extract all the data you need. The effectiveness of your web scraping will depend mostly on clearly defining what elements you want to extract and being able to handle errors.
In the end, the web scraper outputs all of the collected data in a user-friendly format. Most scrapers output data in Excel, JSON, CSV, XML spreadsheets, and other formats that can be used for APIs.
Data Extraction from Dynamic Web Sites
To cope with a dynamic website, you need:
- Use a headless browser, load the URL, and then parse the HTML with an HTML parser. In NodeJS you can use Puppeteer for headless browsing and Cheerio for parsing HTML, and in Python, you can use Selenium for headless browsing and Beautiful Soup for parsing HTML.
- You can use a webdriver. This is a connector to control the browser for tasks such as launching or testing an application on the web. They help you overcome the dependency on the browser to create dynamic websites and automate the process of loading target websites in the browser and on the input request by allowing you to choose a specific browser and version to run the scraping code.
- You can also use an out-of-the-box web scraper. This option is more preferable if the number and complexity of websites for scraping increases over time. Web scraping solutions automate the data collection process. Proposed solutions that integrate IP proxies are particularly useful for overcoming geographic specifications, as they use dynamic proxies that allow you to change the IP address for each request to avoid being blocked by the website.
We offer customized web scraping solutions that can provide any data you need, on time and with no hassle!
Tired of getting blocked while scraping the web?
Get structured data in the format you need!
We offer customized web scraping solutions that can provide any data you need, on time and with no hassle!
What Is Web Scraping Used For?
In the past, scrapers couldn't collect certain types of data, such as images and videos. But over the past few years, web scraping technology has improved significantly and modern scrapers can now automate almost all data collection tasks.
Here are some of the most common examples:
Price Comparison & Monitoring
Web scraping allows you to collect product descriptions and pricing data from around the web to make better pricing decisions. That way you can react quickly to general price changes and optimize your own. For example, to outperform competitors in emerging markets while lowering prices elsewhere. You can monitor shopping trends, analyze competitors' marketing strategies, and comply with MAP and other price regulations.
Finance & Investment
Financial and investment firms use data to make investment decisions. Web scraping helps you analyze current financial market conditions, track emerging trends and analyze their impact, and monitor news affecting stocks and the economy. With scraping, you can analyze company documents and monitor public sentiment about industries.
Real Estate & Property
Real estate web scraping allows agents, brokers, and realtors to evaluate competitors' payment models, track market dynamics on a local, regional and national level, improve their listings relative to competitors, make informed decisions in the market, and soberly assess property values and rental yields. With this data, it will be easier to understand where the market is going and invest wisely.
Current News & Content
With web scraping, you can track current trends related to global and regional affairs or news articles in order to react to them in a timely manner. You can analyze public reactions to trends, make an investment or purchase decisions, monitor competitors and conduct targeted campaigns, such as political ones.
Web scraping can be used when creating digital content, such as blogs and other social media content. Scraped data from social media may help in analyzing how the public reacts to emerging trends and current events.
Web scraping can collect data on people's behavior and Internet communication patterns to later use the information for machine learning projects, training predictive models, and optimizing NLP models.
Machine learning enables technologies such as driverless cars like Tesla, image and speech recognition, and more. This is where websites are indispensable resources for getting raw data to develop and improve models.
How Marketers Use Web Scraping?
Consumer Sentiment Analysis
Web scrapers provide data to advertising agencies and marketing teams by automating the process. Social media scraping is full of diverse opinions about products and social issues, allowing you to track consumer sentiment and understand the values and desires of the audience you're advertising to and providing your product to. The data collected is useful both in the development of new projects and in the improvement of existing ones.
Increasing Brand Awareness
A strong brand sets your product apart from the competition and inspires consumer confidence. Analyzing brand mentions provides insight into how you are currently perceived and how you can adjust your customer service and marketing strategies to improve your reputation and awareness.
Monitor Market Trends
To understand your role in the marketplace, you need to know your competitors. Researching your competitors will allow you to be aware of what you're up against. This way, the data you get will help in researching and understanding current trends. You can analyze and use the weaknesses and strengths of your competitors' marketing campaigns to successfully reach your target audience and offer new solutions.
Is it Legal to Scrape Web Pages?
Web scraping is ubiquitous among both small and large businesses. Nevertheless, the legality associated with it is extremely complex. Web scraping is generally not illegal anywhere in the world, but problems arise when people do not respect intellectual property rights and collect personal data and copyrighted material. When you collect information, you need to make sure that your activities are conducted within the law.
Web scraping is covered by many legal provisions, such as:
- Violation of the Computer Fraud and Abuse Act (CFAA)
- Violation of the Digital Millennium Copyright Act (DMCA)
- Copyright infringement
- Trespass to chattel
For example, Amazon scraping is not illegal and is already part of many companies models. Scraping data from Facebook and Instagram isn't illegal either, but the use of personal data is restricted.
Is Web Scraping Free?
There are many both paid and free web scraping tools and services available. They can also be designed for both programmers and non-programmers. For example, we, like most services, provide a free version so you can see if the scraper is right for you or not and use all the benefits of automatic data collection. The free programs are easy to use and will satisfy most web scraping needs with a reasonable amount of data required.
What Are Web Scraping Tools?
Web scraping tools automate data collection from the web. Web scraping tools easily turn a complicated, messy case into an easy one and save you time and effort. There are several factors to consider when choosing a web scraper:
- Ease of use. It is best to choose a scraper that is easy to set up and use and that has a complete user interface.
- Scalability. A good scraper should maintain high performance regardless of the project size.
- IP rotation. This way you can easily scrape data without having your IP address blacklisted from websites.
- Multiple output formats. Get a scraper that provides a variety of options for exporting collected data.
As we mentioned earlier, web scraping tools can be in the form of a browser extension or installed software. You can also use services offered by other companies. For example, you can use our API for data scraping.
The Dark Side of Data Scraping
Although web scraping can be used for good, there are those who abuse it.
The risks of disclosing user data can spread in a variety of ways, as criminals can develop malicious scrapers with features to bypass the protection of targeted websites and collect more sensitive information from platform users.
The most common misuse of scraping is the collection of people's email addresses, which are then sold to third parties or scammers. In some jurisdictions, it is illegal to use automated data collection tools to collect email addresses for commercial purposes.
Of course, social media platforms are particularly susceptible to criminal data collection because of the vast amount of personally identifiable information that users regularly share. In the case of the Facebook and Linkedin data clipping incident, the database did contain personal information such as phone numbers and email addresses. If cybercriminals get hold of this data, they can use it for phishing and other types of fraud.
The Internet is evolving at a rapid pace, and businesses are becoming increasingly data-dependent. Access to the latest information on any subject has become the basis for decision-making by organizations. And those who make advanced use of web scraping will get ahead of their competitors and take the lead in the marketplace.
We hope this article has helped you better understand what web scraping is and what it's for. In the following articles, we'll talk more about each of the ways it can be used.