Crawl Data

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 crawling legal? – Towards Data Science

Photo by Sebastian Pichler on UnsplashWeb crawling, also known as web scraping, data scraping or spider, is a computer program technique used to scrape a huge amount of data from websites where regular-format data can be extracted and processed into easy-to-read structured crawling basically is how the internet functions. For example, SEO needs to create sitemaps and gives their permissions to let Google crawl their sites in order to make higher ranks in the search results. Many consultant companies would hire companies to specialize in web scraping to enrich their database so as to provide professional service to their is really hard to determine the legality of web scraping in the era of the digitized crawling can be used in the malicious purpose for example:Scraping private or classified information. Disregard of the website’s terms and service, scrape without owners’ abusive manner of data requests would lead web server crashes under additionally heavy is important to note that a responsible data service provider would refuse your request if:The data is private which would need a username and passcodesThe TOS (Terms of Service) explicitly prohibits the action of web scrapingThe data is copyrightedViolation of the Computer Fraud and Abuse Act (CFAA). Violation of the Digital Millennium Copyright Act (DMCA)Trespass to “just scraped a website” may cause unexpected consequences if you used it probably heard of the HiQ vs Linkedin case in 2017. HiQ is a data science company that provides scraped data to corporate HR departments. Linkedin then sent desist letter to stop HiQ scraping behavior. HiQ then filed a lawsuit to stop Linkedin from blocking their access. As a result, the court ruled in favor of HiQ. It is because that HiQ scrapes data from the public profiles on Linkedin without logging in. That said, it is perfectly legal to scrape the data which is publicly shared on the ’s take another example to illustrate in what case web scraping can be harmful. The law case eBay v. Bidder’s Edge. If you’re doing web crawling for your own purposes, it is legal as it falls under fair use doctrine. The complications start if you want to use scraped data for others, especially commercial purposes. Quoted from, 100 1058 (N. D. Cal. 2000), was a leading case applying the trespass to chattels doctrine to online activities. In 2000, eBay, an online auction company, successfully used the ‘trespass to chattels’ theory to obtain a preliminary injunction preventing Bidder’s Edge, an auction data aggregation, from using a ‘crawler’ to gather data from eBay’s website. The opinion was a leading case applying ‘trespass to chattels’ to online activities, although its analysis has been criticized in more recent long as you are not crawling at a disruptive rate and the source is public you should be fine. I suggest you check the websites you plan to crawl for any Terms of Service clauses related to scraping their intellectual property. If it says “no scraping or crawling”, you should respect ggestion:Scrape discreetly, check “” before you start scrapingGo conservative. Aggressively asking for data can burden the internet server. An ethical way is to be gentle. No one wants to crash the the data wisely. Don’t duplicate the data. You can generate insight from collected data, and help Your business out to the owner of the website before you start ’t randomly pass scraped data to anyone. If it is valuable data, keep it secure.

Best 3 Ways to Crawl Data from a Website | Octoparse

The need for crawling web data has become larger in the past few years. The data crawled can be used for evaluation or prediction in different fields. Here, I’d like to talk about 3 methods we can adopt to crawl data from a website.
1. Use Website APIs
Many large social media websites, like Facebook, Twitter, Instagram, StackOverflow provide APIs for users to access their data. Sometimes, you can choose the official APIs to get structured data. As the Facebook Graph API shows below, you need to choose fields you make the query, then order data, do the URL Lookup, make requests and etc. To learn more, you can refer to
2. Build your own crawler
However, not all websites provide users with APIs. Certain websites refuse to provide any public APIs because of technical limit or other reasons. Someone may propose RSS feeds, but because they put a limit on their use, I will not suggest or make further comments on it. In this case, what I want to discuss is that we can build a crawler on our own to deal with this situation.
How does a crawler work? A crawler, put it another way, is a method to generate a list of URLs that you can feed through your extractor. The crawlers can be defined as tools to find the URLs. You first give the crawler a webpage to start, and they will follow all these links on that page. Then this process will keep going on in a loop.
Read about:
Believe It Or Not, PHP Is Everywhere
The Best Programming Languages for Web Crawler: PHP, Python or
How to Build a Crawler to Extract Web Data without Coding Skills in 10 Mins
Then, we can proceed with building our own crawler. It’s known that Python is an open-source programming language, and you can find many useful functional libraries. Here, I suggest the BeautifulSoup (Python Library) for the reason that it is easier to work with and possesses many intuitive characters. More exactly, I will utilize two Python modules to crawl the data.
BeautifulSoup does not fetch the web page for us. That’s why I use urllib2 to combine with the BeautifulSoup library. Then, we need to deal with HTML tags to find all the links within page’s tags and the right table. After that, iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading