- HTTP & SOCKS
- Price $1.3/IP
- Locations: DE, RU, US
- 5% OFF coupon: APFkysWLpG
Web Scraper – The #1 web scraping extension
400, 000 users are proud of using our solutions!
Point and click
Our goal is to make web data extraction as simple as possible.
Configure scraper by simply pointing and clicking on elements.
No coding required.
Extract data from dynamic
Web Scraper can extract data from sites with multiple levels of navigation. It can navigate a
website on all levels.
Categories and subcategories
Built for the modern web
are less accessible to scrapers. Web Scraper solves this by:
Waiting for Ajax requests
Page scroll down
Modular selector system
Web Scraper allows you to build Site Maps from different types of selectors.
This system makes it possible to tailor data extraction to different site structures.
Export data in CSV, XLSX and JSON
Build scrapers, scrape sites and export data in CSV format directly from your browser.
Use Web Scraper Cloud to export data in CSV, XLSX and JSON formats, access it via API, webhooks or
get it exported via Dropbox.
Simply AMAZING. Was thinking about coding myself a simple scraper for a project
and then found this super easy to use and very powerful scraper. Worked
perfectly with all the websites I tried on. Saves a lot of time. Thanks for
Powerful tool that beats the others out there. Has a learning curve to it but
once you conquer that the sky’s the limit. Definitely a tool worth making a
donation on and supporting for continued development. Way to go for the
authoring crew behind this tool.
This is fantastic! I’m saving hours, possibly days. I was trying to scrap and old
site, badly made, no proper divs or markup.
Using the WebScraper magic, it somehow “knew” the pattern after I selected 2
Yes, it’s a learning curve and you HAVE to watch the video and read the docs.
Don’t rate it down just because you can’t be bothered to learn it. If you put
the effort in, this will save your butt one day!
- HTTP & SOCKS
- Price $1.3/IP
- Locations: DE, RU, US
- 5% OFF coupon: APFkysWLpG
Pulling Data from the Web: How to Get Data from a Website
The value of web data is increasing in every industry from retail competitive price monitoring to alternative data for investment research. Getting that data from a website is vital to the success of your business. As the trusted research firm, Gartner, stated in their blog:
“Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself…Treat the Internet itself as your organization’s largest data source. ”
In fact, the internet is the largest source of business data on earth and it’s growing by the minute. The infograph below from Domo shows how much web data is created every minute from just a few websites out of a billion.
It’s clear the need for web data integration is greater than ever. This article will walk you through a simple process of pulling data from a webpage using data extraction software. First, let’s look at other uses of web data in business.
How do businesses use data from a website?
Competitive price comparison and alternative data for equity research are two popular uses of website data, but there are others less obvious.
Here are a few examples:
Teaching Movie Studios how to spot a hit manuscript
For StoryFit, data is the fuel that powers its predictive analytic engines. StoryFit’s artificial intelligence and machine learning algorithms are trained using vast amounts of data culled from a variety of sources, including extractors. This data contributes to StoryFit’s core NLP-focused AI to train machine learning models to determine what makes a hit movie.
Predicative Shipping Logistics
ClearMetal is a Predictive Logistics company using data science to unlock unprecedented efficiencies for global trade. They are using web data to mine all container and shipping information in the world then feed predictions back to companies that run terminals.
XiKO provides market intelligence around what consumers say online about brands and products. This information allows marketers to increase the efficacy of their programs and advertising. The key to XiKO’s success lies in its ability to apply linguistic modeling to vast amounts of data collected from websites.
Virtuance uses web data to review listing information from real estate sites to determine which listings need professional marketing and photography. From this data, Virtuance determines who needs their marketing services and develops success metrics based on the aggregated data.
Now that you have some examples of what companies are doing with web data, below are the steps that will show you how to pull data from a website.
Steps to get data from a website
Websites are built for human consumption, not machine. So it’s not always easy to get web data into a spreadsheet for analysis or machine learning. Copying and pasting information from websites is time-consuming, error-prone and not feasible.
Web scraping is a way to get data from a website by sending a query to the requested page, then combing through the HTML for specific items and organizing the data. If you don’t have an engineer on hand, provides a no-coding, point and click web data extraction platform that makes it easy to get web data.
Here’s a quick tutorial on how it works:
Step 1. First, find the page where your data is located. For instance, a product page on
Step 1. First, find the page where your data is located.
Step 2. Copy and paste the URL from that page into, to create an extractor that will attempt to get the right data.
Step 2. Copy and paste the URL from that page into
Step 3. Click Go and will query the page and use machine learning to try to determine what data you want.
Step 4. Once it’s done, you can decide if the extracted data is what you need. In this case, we want to extract the images as well as the product names and prices into columns. We trained the extractor by clicking on the top three items in each column, which then outlines all items belonging to that column in green.
Step 4. Once it’s done, you can decide if the extracted data is what you need.
Step 5. then populates the rest of the column for the product names and prices.
Step 6. Next, click on Extract data from website.
Step 7. has detected that the product listing data spans more than one page, so you can add as many pages as needed to ensure that you get every product in this category into your spreadsheet.
Step 8. Now, you can download the images, product names, and prices.
Step 9. First, download the product name and price into an Excel spreadsheet.
Step 10. Next, download the images as files to use to populate your own website or marketplace.
What else can you do with web scraping?
This is a very simple look at getting a basic list page of data into a spreadsheet and the images into a Zip folder of image files.
There’s much more you can do, such as:
Link this listing page to data contained on the detail pages for each product.
Schedule a change report to run daily to track when prices change or items are removed or added to the category.
Compare product prices on Amazon to other online retailers, such as Walmart, Target, etc.
Visualize the data in charts and graphs using Insights.
Feed this data into your internal processes or analysis tools via the APIs.
Web scraping is a powerful, automated way to get data from a website. If your data needs are massive or your websites trickier, offers data as a service and we will get your web data for you.
No matter what or how much web data you need, can help. We offer the world’s only web data integration platform which not only extracts data from a website, it identifies, prepares, integrates, and consumes it. This platform can meet an organization’s consumption needs for business applications, analytics, and other processes. You can start by talking to a data expert to determine the best solution for your data needs, or you can give the platform a try yourself. Sign up for a free seven day trial, or we’ll handle all the work for you.
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is
Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
Get the latest from imperva
The latest news from our experts in the fast-changing world of application, data, and edge security.
Subscribe to our blog
Frequently Asked Questions about website extract
How do I extract content from a website?
Steps to get data from a websiteFirst, find the page where your data is located. … Copy and paste the URL from that page into Import.io, to create an extractor that will attempt to get the right data. … Click Go and Import.io will query the page and use machine learning to try to determine what data you want.More items…•Aug 9, 2018
Is it legal to scrape a website?
Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
How can I extract data from a website for free?
Besides that, the cloud service will allow you to store and retrieve the data at any time.ScrapingBot.Data Scraper (Chrome)Web scraper.Scraper (Chrome)Outwit hub(Firefox)Dexi.io (formerly known as Cloud scrape)Webhose.io.Aug 3, 2021