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Web Scraping is known by many other names, depending on how a company likes to call it, Screen Scraping, Web Data Extraction, Web Harvesting and more, is a technique employed to extract large amounts of data from websites.
Scraping is the process where the data is extracted from various websites and repositories and are saved locally for instantaneous use or analysis that is to be performed later on. Data is saved to a local file system or database tables, as per the structure of the data extracted.
Most websites, that we view regularly, allow us only to see the contents and do not generally allow a copy or download facility. Manually copying the data is as good as cutting newspapers and can take days and weeks.
Web Scraping is the technique of automating this process so that an intelligent script can help you extract data from web pages of your choice and save them in a structured format.
A web scrapping software will automatically load multiple web pages one by one, and extract data, as per requirements. It is either custom-built for a specific website or is one, which can be configured, based on a set of parameters, to work with any website. With the click of a button, you can easily save the data available on a website to a file on your computer.
In today’s world, intelligent bots do web scraping. Unlike screen scraping, which only copies whatever the pixels display on a screen, these bots extract underlying HTML code, as well as the data stored in a database in the background.
Businesses crawl eCommerce websites for prices, product descriptions, and images, to get all the data possible so as to boost analytics and predictive modeling. Price comparison in recent years has made it very important for businesses to know the rates of their competitors. Unless the rates are competitive, e-commerce websites can go out of business in no time. Even travel websites have been extracting prices from airlines’ websites for a long time. Custom web page scraping solutions will help you get all the variable data fields that you might need. This way you can collect data and create your own data warehouse, for current as well as future use.
This helps in scraping data related to an individual or a company. This data is later on used for analytics, comparisons, investment decisions, hiring, and more. Many companies today crawl job boards for such use cases.
Intended specifically for new websites/channels where the scraped data can help understand public demand and behavior. It helps new companies, to begin with, activities and products based on pattern discoveries that will gain more organic visits. This way, they will have to spend less on advertisements.
Online reputation is very important today since many businesses depend on word of mouth to help them grow. Here, scraping from social media helps to understand current public opinion and sentiments. Then the company can even do small things that have a big social impact. Opinion leaders, trending topics, and demographic facts can be prominent through data scraping, and then these can be used to make sure that the company can repair its image, or have a greater online “public-satisfaction score”.
Online reviews help the new age online-shoppers decide what to buy, and where to buy from, be it a refrigerator or a car. Hence, these reviews have a lot of importance. Opinion Spamming refers to “illegal” activities example writing fake reviews on the portals. It is also called shilling – an activity that aims to deceive online buyers. Thus, website scraping can help crawl the reviews and detect which one to block, or which one to verify, because such reviews generally stand out among the crowd.
Scraping not only gives numbers to crunch but also helps a company to understand which add would be more suitable for which internet users. This helps save marketing revenue while it also attracts hits that often are converted.
Businesses are able to get more services under a single umbrella to attract more customers. For example, if you open an online health portal and scrap and use data related to all doctors, pharmacies, nursing homes, and hospitals nearby, then you will be able to attract many people to your website.
Media websites need to be instantly updated on the breaking news as well as other trending information that people are accessing on the internet. Often, the websites that are among the first to publish a story, get the most hits. Web scraping helps monitor popular forums and also grab trending topics and more.
The contents, style, and structure of an XML file are defined in the DOM, short for Document Object Model. Scrapers that need to know the internal working of a web-page and extract scripts running deep inside, that have been abstracted, generally use DOM parsers. The specific nodes are gathered using DOM parsers and tools like XPath helps to crawl the web pages. Even if the content generated is dynamic in nature, DOM parsers come to the rescue.
Companies with huge computing power, targeting specific verticals, create vertical aggregation platforms. Some even run these data harvesting platforms on the cloud. Bots are created and monitored, for specific verticals, and businesses in these platforms, with the need for virtually no human intervention. The pre-existing knowledge base for a vertical helps create bots efficiently, for it, and the performance of the bots, thus created, tend to be much better.
XML Path Language or XPath is a query language that is used when extracting data from nodes of XML documents. XML documents follow a tree-like structure and XPATH is an easy way to access specific nodes and extract data from those nodes. XPath is used along with DOM parsing to extract data from websites, no matter they are static or dynamic.
This is a regular expression-matching technique (commonly called regex in the coding community), using the UNIX grep command. It is generally clubbed with popular programming languages like Perl, and more recently Python- beautiful soup.
Numerous web scraping software and services are available in the market, and there is no need to be a master in all the above-mentioned techniques. There are also tools like CURL, HTTrack, Wget, Node.js, and more.
Outsourcing your web data extraction needs to a service provider dealing with data is the most recommended and the easiest way to quench your business’ hunger for data. When your data provider helps you with the extraction and cleaning of data, you get rid of the need for a completely separate dedicated team for tackling data woes and can remain relieved. Both the software as well as the infrastructure needs that your company’s data extraction techniques need are taken care of, by them, and since these companies are extracting data for clients on a regular basis, you would never have a problem that they haven’t solved, or at least faced already. All you need to do is provide them with your requirements and then sit back as they spin their magic and hand you your priceless data.
You can also go on for an in house data extraction if your company is technically rich. Not only would you be needing skilled individuals having worked in web-scraping projects and experts in R and Python, but you would also need the cumbersome infrastructure to be set up so that your team can scrap websites, all day and night.
Web crawlers tend to break even with the slightest change in the web-pages that they are targeting and due to this web-scraping is never a do and forget solution. You need the dedicated team to be working on solutions all the time, and sometimes, they might anticipate a big change coming in the way that webpages are storing data, and then they need to be prepared for the same.
Both building and maintaining a web-scraping team are complex tasks and should be undertaken only if your company has sufficient resources.
Data providers that cater only to a specific industry vertical are there in hordes, and these Vertical specific data extraction solutions are great if you can find one that covers your data needs. Since your service provider would only be working in a single domain, chances are, that they will be extremely skilled in that domain. The data sets might vary and the solutions they might provide you might be highly customizable based on your needs. They may be able to provide you different packages based on your company size and budgets too.
For the ones that do not have the budget for an in-house web crawling team and neither take the help of a DaaS provider, they are left with DIY tools that are easy to learn and simple to use. However, the serious downside is that you cannot extract too many pages at one go. They are often too slow for mass data extraction, and they might not be able to parse sites that use more complex rendering techniques.
There are several different methods and technologies that can be used to build a crawler and extract data from the web. Following is the basic structure of a scraping website setup.
It is a tree traversal like procedure, where the crawler first goes through the seed URL or the base URL, and then looks for the next URL in the data that is fetched from the seed URL and so on. The seed URL would be hard-coded in at the very beginning. For example, to extract all the data from the different pages of a website, the seed URL would serve as an unconditional base.
Once the data from the seed URL has been extracted and stored in the temporary memory, the hyperlinks present in the data need to be given to the pointer and then the system should focus on extracting data from those.
The crawler needs to extract and store all the pages that it parses, while traversing in a single repository, like HTML files. The final step of data extracting and data cleaning actually happens in this local repository.
All the data that you might need is now in your repository. But the data is not usable. So you would need to teach the crawler to identify data points and extract only the data that you will be needing.
Noise-less data should only be extracted and duplicate entries should be deleted by the scraper automatically. Such things should be built into the intelligence of the scraper so as to make it handier, and the data coming from it as output, more usable.
Only if the scraper is able to structure the unstructured scraped data, will you be able to create a pipeline to directly feed the result of your scraping mechanism to your business.
Although a great tool for gaining insights, there are a few legal aspects that you should take care of so that you do not get into trouble.
Always check the Robots.txt file, of whichever website you plan to crawl. The document has a set of rules that define how bots can interact with the website, and scraping in a manner that goes against these rules can lead to lawsuits and fines.
Do not become a frequent hitter. Web servers end up falling prey to downtime if the load is very high. Bots add load to a website’s server and if the load exceeds a certain point, the server might become slow or crash destroying the great user experience of a website.
To avoid getting caught up in web-traffic and server downtime, you can crawl at night, or at times when you see that the traffic for a website is less.
Policies should be honored and publishing of data that is copyrighted might have severe repercussions. So it is better that you use the scraped data responsibly.
One aspect of data scraping that bugs a lot of people is how to find reliable websites to crawl. Some quick points to note:
Links are the main food for your web-scraping software. You do not want broken links to break the streamlined flow of processes.
These sites are difficult to scrap and keep changing. Hence the scraper might break in the middle of a task.
Ensure that the sites you crawl are known to be reliable and have fresh data.
Whether you are selling or buying goods, or trying to increase the user base for your magazine, whether you are a company of fifty or five hundred, chances are, eventually you will need to surf on the waves of data if you want to remain in the competition. In case you are a technology-based company with huge revenue and margins, you might even start your own team to crawl, clean and model data.
However, here I will be providing more of a generalized approach, applicable to all. With the advent of newly coined flashy words and technological marvels, people forget the main thing – Business. First, you need to decide which business problem you are trying to solve. It might be the fact that a competitor is growing much faster than you are and you need to get back in the game. It might be that you need access to more trending topics and words to get more organic hits, or to sell more magazines. Your problem might be so unique that no other business has faced it before.
In the next step, you need to identify, what type of data you would need to solve that problem. You need to answer questions like- “Do you have a sample of the type of data that you would need?” or “Which are the websites, which when scraped would benefit you most?” Then you will have to decide on how to get the job done. Setting up a data scraping team all of a sudden is madness, and it can in no way, done in a hurry. You are better off if you get someone to do it for you, someone like PromptCloud, who have years of experience and have worked with multiple customers, to solve a variety of problems in the extraction of web data through scraping.
So no matter which path you take to your data, remember –
“War is ninety percent information.”
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