Web Scraping Vs Web Crawling

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What Is The Difference Between Web Crawling And Web ...

What Is The Difference Between Web Crawling And Web …

This article will help you match your use case to the correct data collection methodology as well as understanding the key advantages and challenges of each option.
30-Nov-2020
In this article we will discuss:
Web crawling Vs. Web scraping
Common web scraping use cases
What are the advantages of each option?
Main challenges
Web crawling, also known as Indexing is used to index the information on the page using bots also known as crawlers. Crawling is essentially what search engines do. It’s all about viewing a page as a whole and indexing it. When a bot crawls a website, it goes through every page and every link, until the last line of the website, looking for ANY information.
Web Crawlers are basically used by major search engines like Google, Bing, Yahoo, statistical agencies, and large online aggregators. The web crawling process usually captures generic information, whereas web scraping hones in on specific data set snippets.
Web scraping, also known as web data extraction, is similar to web crawling in that it identifies and locates the target data from web pages. The key difference, is that with web scraping, we know the exact data set identifier e. g. an HTML element structure for web pages that are being fixed, from which data needs to be extracted.
Web scraping is an automated way of extracting specific data sets using bots which are also known as ‘scrapers’. Once the desired information is collected it can be used for comparison, verification, and analysis based on a given business’s needs and goals.
Here are some of the most popular ways in which businesses leverage web scraping to attain their business goals:
Research: Data is often an integral part of any research project whether it is purely academic in nature or for marketing, financial, or other business applications. The ability to collect user data in real-time and identify behavioral patterns, for example, can be paramount when trying to stop a global pandemic or identify a specific target audience.
Retail / eCommerce: Companies, especially in the eCom space need to regularly perform market analyses in order to maintain a competitive edge. Relevant data sets that both front and backend retail businesses collect include pricing, reviews, inventory, special offers, and the like.
Brand Protection: Data collection is becoming an integral part of protecting against brand fraud, and brand dilution as well as identifying malicious actors who are illegally profiting from corporate intellectual property (names, logos, item reproductions). Data collection helps companies monitor, identify, and take action against such cybercriminals.
Key web scraping benefits
Highly accurate – Web scrapers help you eliminate human errors from your operations so that you can be confident that the information you receive is 100% accurate.
Cost-efficient– Web scraping can be more cost-effective as more often than not you will need less staff to operate and in many cases, you will be able to gain access to a completely automated solution that requires zero infrastructure on your end.
Pinpointed – Many web scrapers allow you to filter for exactly the data points you are looking for meaning you can decide that on a specific job they collect images and not videos or pricing and not descriptions. This can help you save time, bandwidth, and money over the long term.
Key data crawling benefits
Deep dive – This method involves an in-depth indexation of every target page. This can be useful when trying to uncover and collect information in the deep underbelly of the World Wide Web.
Real-time– Web crawling is preferable for companies looking for a real-time snapshot of their target data sets as they are more easily adaptable to current events.
Quality assurance– Crawlers are better at content quality assessment meaning it is a tool that provides an advantage when performing QA tasks for example.
Despite their difference web crawling and web scraping share some mutual challenges:
#1: Data blockades– Many websites have anti-scraping/crawling policies which can make it challenging to collect the data points you need. A web scraping service can sometimes be extremely effective in this instance especially if they give you access to large proxy networks that can help you collect data using real user IPs and circumvent these types of blocks.
#2: Labor-intensive– Performing data crawling/scraping jobs at scale can be very labor-intensive and time-consuming. Companies who may have started off needing data sets once in a while but now need a regular flow of data, can no longer rely on manual collections.
#3: Collection limitations– Performing data scraping/crawling can usually be easily accomplished for simple target sites but when you start encountering tougher target sites, some IP blocks can be insurmountable.
Summing it up
Now that you know the difference between web crawling and web scraping all you need to do is choose which of them is most effective for your specific use case. You need to determine your budget and whether you have an in-house staff who can manage your data collection process or if you prefer outsourcing this to a data collection network.
Yair Ida | Sales Director Yair is a Sales Director at Bright Data. He specializes as a growth strategist and works in the fields of SaaS business development, sales, and marketing. He is a self-proclaimed ‘data entrepreneur’ with a deep knowledge of software products that he works with in order to help businesses create scalable, efficient, and cost-effective data collection processes.
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Data Crawling vs Data Scraping - The Key Differences

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Data Crawling vs Data Scraping – The Key Differences

One of our favourite quotes has been, ‘If a problem changes by an order, it becomes a different problem’ and in this lies the answer to – Data Crawling vs Data Scraping.
Data Crawling means dealing with large data sets where you develop your crawlers (or bots) which crawl to the deepest of the web pages. Data scraping, on the other hand, refers to retrieving information from any source (not necessarily the web). It’s more often the case that irrespective of the approaches involved, we refer to extracting data from the web as scraping (or harvesting) and that’s a serious misconception.
Data Crawling vs Data Scraping – Key Differences
1. Scraping data does not necessarily involve the web. Data scraping tools that help in data scraping could refer to extracting information from a local machine, a database. Even if it is from the internet, a mere “Save as” link on the page is also a subset of the data scraping universe. Data crawling, on the other hand, differs immensely in scale as well as in range. Firstly, crawling = web crawling which means on the web, we can only “crawl” data. Programs that perform this incredible job are called crawl agents or bots or spiders (please leave the other spider in spiderman’s world). Some web spiders are algorithmically designed to reach the maximum depth of a page and crawl them iteratively (did we ever say crawl? ). While both seem different, web scraping vs web crawling is mostly the same.
2. The web is an open world and the quintessential practising platform of our right to freedom. Thus a lot of content gets created and then duplicated. For instance, the same blog might be posted on different pages and our spiders don’t understand that. Hence, data de-duplication (affectionately dedup) is an integral part of web data crawling service. This is done to achieve two things — keep our clients happy by not flooding their machines with the same data more than once; and saving our servers some space. However, deduplication is not necessarily a part of web data scraping.
3. One of the most challenging things in the web crawling space is to deal with the coordination of successive crawls. Our spiders have to be polite with the servers, that they do not piss them off when hit. This creates an interesting situation to handle. Over some time, our spiders have to get more intelligent (and not crazy! ). They get to develop learning to know when and how much to hit a server, how to crawl data feeds on its web pages while complying with its politeness policies. While both seem different, web scraping vs web crawling is mostly the same.
4. Finally, different crawl agents are used to crawling different websites and hence you need to ensure they don’t conflict with each other in the process. This situation never arises when you intend to just crawl data.
Data ScrapingData CrawlingInvolves extracting data from varioussources including webRefers to downloading pages from thewebCan be done at any scaleMostly done at a large scaleDeduplication is not necessarily a partDeduplication is an essential partNeeds crawl agent and parserNeeds only crawl agent
On a concluding note, when talking about web scraping vs web crawling. ‘Scraping’ represents a very superficial node of crawling which we call extraction, and that again requires few algorithms and some automation in place.
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

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.
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Frequently Asked Questions about web scraping vs web crawling

What is the difference between data scraping and data crawling?

Data Crawling means dealing with large data sets where you develop your crawlers (or bots) which crawl to the deepest of the web pages. Data scraping, on the other hand, refers to retrieving information from any source (not necessarily the web).

Is web scraping legal?

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. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.

Do hackers use web scraping?

For this purpose smart web scraping is your number one growth hacker tool. Developing strong, reliable leads has always been a key feature of web scraping, and it’s as simple as understanding where your target audience is active online and scraping those sites for specific information.

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