Web Scraping & Techniques
Why Web Scraping is Popular?
Web Scraping provides something really valuable that nothing else can gives you structured web data from any public website.
More than a modern convenience, the true power of data web scraping lies in its ability to build and power some of the world’s most revolutionary business applications. ‘Transformative’ doesn’t even begin to describe the way some companies use web scraped data to enhance their operations, informing executive decisions all the way down to individual customer service experiences.
What is Web Scraping?
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.
If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.
Can i see few sample of website scraping tools?
Yes, You can follow below links for Web Scraping tools.
>> Web Scraping using Nodejs, Axios, Cheerio
>> Flipkart Scraping using Python
How do you use a Data Scraper?
Whether you’re using a data scraper tool yourself or outsourcing the job to a web data extraction specialist, you’ll need to know a bit more about the differences between web crawling and web scraping. Just as importantly, you’ll need to understand the possible pitfalls of extraction and how to avoid them. Read on to find out how web scraping works and how to achieve it successfully.
What is Data Scraping Good for?
Web data extraction — also widely known as data scraping — has a huge range of applications. A data scraping tool can help you automate the process of extracting information from other websites, quickly and accurately. It can also make sure the data you’ve extracted is neatly organized, making it easier to analyze and use for other projects.
In the world of e-commerce, web data scraping is widely used for competitor price monitoring. It’s the only practical way for brands to check the pricing of their competitors’ products and services, allowing them to fine-tune their own price strategies and stay ahead of the game. It’s also used as a tool for manufacturers to ensure retailers are compliant with pricing guidelines for their products. Market research organizations and analysts depend on web data extraction to gauge consumer sentiment by keeping track of online product reviews, news articles, and feedback.
There’s a vast array of applications for data extraction in the financial world. Data scraping tools are used to extract insight from news stories, using this information to guide investment strategies. Similarly, researchers and analysts depend on data extraction to assess the financial health of companies. Insurance and financial services companies can mine a rich seam of alternative data scraped from the web to design new products and policies for their customers.
Applications for web data extraction don’t end there. Data scraping tools are widely used in news and reputation monitoring, journalism, SEO monitoring, competitor analysis, data-driven marketing and lead generation, risk management, real estate, academic research, and much more.
Basics of Web Scraping
It’s extremely simple, in truth, and works by way of two parts: a web crawler and a web scraper. The web crawler is the horse, and the scraper is the chariot. The crawler leads the scraper, as if by hand, through the internet, where it extracts the data requested.
Ultimate Crawler
A web crawler, which we generally call a “spider,” is an artificial intelligence that browses the internet to index and search for content by following links and exploring, like a person with too much time on their hands. In many projects, you first “crawl” the web or one specific website to discover URLs which then you pass on to your scraper.
The Scraper
A web scraper is a specialized tool designed to accurately and quickly extract data from a web page. Web scrapers vary widely in design and complexity, depending on the project. An important part of every scraper is the data locators (or selectors) that are used to find the data that you want to extract from the HTML file usually, XPath, CSS selectors, regex, or a combination of them is applied.
What is a Scraping Tool?
A web scraping tool is a software program that’s designed specifically to extract (or ‘scrape’) relevant information from websites. You’ll almost certainly be using some kind of scrape tool whenever you are collecting data from web pages programmatically.
A scraping tool typically makes HTTP requests to a target website and extracts the data from a page. Usually, it parses content that is publicly accessible and visible to users and rendered by the server as HTML. Sometimes it also makes requests to internal application programming interfaces (APIs) for some associated data like product prices or contact details, that are stored in a database and delivered to a browser via HTTP requests.
There are various kinds of web scrape tools out there, with capabilities that can be customized to suit different extraction projects. For example, you might need a scraping tool that can recognize unique HTML site structures, or extract, reformat and store data from APIs.
Scraping tools can be large frameworks designed for all kinds of typical scraping tasks, but you can also use general-purpose programming libraries and combine them to create a scraper.
For example, you might use an HTTP requests library such as the Python-Requests library and combine it with the Python BeautifulSoup library to scrape data from your page. Or you may use a dedicated framework that combines an HTTP client with an HTML parsing library. One popular example is Scrapy, an open-source library created for advanced scraping needs.
The Web Data Scraping Process
If you do it yourself using website scraping tools
This is what a general DIY web scraping process looks like:
- Identify the target website
- Collect URLs of the pages where you want to extract data from
- Make a request to these URLs to get the HTML of the page
- Use locators to find the data in the HTML
- Save the data in a JSON or CSV file or some other structured format
You can follow below links for sample of web scraping.
>> Web Scraping using Nodejs, Axios, Cheerio
>> Flipkart Scraping using Python
Simple enough, right? It is! If you just have a small project, But unfortunately, there are quite a few challenges you need to tackle if you need data at scale. For example, maintaining the scraper if the website layout changes, managing proxies, executing javascript, or working around antibots. These are all deeply technical problems that can eat up a lot of resources. There are multiple open-source web data scraping tools that you can use but they all have their limitations. That’s part of the reason many businesses choose to outsource their web data projects.
If you outsource it
1. Our team gathers your requirements regarding your project.
2. Our veteran team of web data scraping experts writes the scraper(s) and sets up the infrastructure to collect your data and structure it based on your requirements.
3. Finally, we deliver the data in your desired format and desired frequency.
Ultimately, the flexibility and scalability of web scraping ensure your project parameters, no matter how specific, can be met with ease. Fashion retailers inform their designers with upcoming trends based on web scraped insights, investors time their stock positions, and marketing teams overwhelm the competition with deep insights, all thanks to the burgeoning adoption of web scraping as an intrinsic part of everyday business.
What can I use instead of a scraping tool?
For all but the smallest projects, you’ll need some kind of automated web scraping tool or data extraction software to obtain information from websites.
In theory, you could manually cut and paste information from individual web pages into a spreadsheet or another document. But you’ll find this to be laborious, time-consuming, and error-prone if you’re trying to extract information from hundreds or thousands of pages.
A web scraping tool automates the process, efficiently extracting the web data you need and formatting it in some kind of neatly-organized structure for storage and further processing.
Another route could be buying the data you need from a data services provider who will extract it on your behalf. This would be useful for big projects involving tens of thousands of web pages.
What is web scraping used for?
Price intelligence
In our experience, price intelligence is the biggest use case for web scraping. Extracting product and pricing information from e-commerce websites, then turning it into intelligence is an important part of modern e-commerce companies that want to make better pricing/marketing decisions based on data.
How web pricing data and price intelligence can be useful:
- Dynamic Pricing
- Revenue Optimization
- Competitor Monitoring
- Product trend Monitoring
- Brand and MAP Compliance
Market Research
Market research is critical and should be driven by the most accurate information available. High quality, high volume, and highly insightful web scraped data of every shape and size is fueling market analysis and business intelligence across the globe.
- Market Trend Analysis
- Market Pricing
- Optimizing Point of Entry
- Research & Development
- Competitor Monitoring
Alternative Data for Finance
Unearth alpha and radically create value with web data tailored specifically for investors. The decision-making process has never been as informed, nor data as insightful and the world’s leading firms are increasingly consuming web scraped data, given its incredible strategic value.
- Extracting Insights from SEC Filings
- Estimating Company Fundamentals
- Public Sentiment Integrations
- News Monitoring
Real Estate
The digital transformation of real estate in the past twenty years threatens to disrupt traditional firms and create powerful new players in the industry. By incorporating web scraped product data into everyday business, agents and brokerages can protect against top-down online competition and make informed decisions within the market.
- Appraising Property Value
- Monitoring Vacancy Rates
- Estimating Rental Yields
- Understanding Market Direction
News & Content Monitoring
Modern media can create outstanding value or an existential threat to your business — in a single news cycle. If you’re a company that depends on timely news analyses, or a company that frequently appears in the news, web scraping news data is the ultimate solution for monitoring, aggregating, and parsing the most critical stories from your industry.
- Investment Decision Making
- Online Public Sentiment Analysis
- Competitor Monitoring
- Political Campaigns
- Sentiment Analysis
Lead generation
Lead generation is a crucial marketing/sales activity for all businesses. In the 2020 Hubspot report, 61% of inbound marketers said generating traffic and leads was their number 1 challenge. Fortunately, web data extraction can be used to get access to structured lead lists from the web.
Brand Monitoring
In today’s highly competitive market, it’s a top priority to protect your online reputation. Whether you sell your products online and have a strict pricing policy that you need to enforce or just want to know how people perceive your products online, brand monitoring with web scraping can give you this kind of information.
Business Automation
In some situations, it can be cumbersome to get access to your data. Maybe you need to extract data from a website that is your own or your partner’s in a structured way. But there’s no easy internal way to do it and it makes sense to create a scraper and simply grab that data. As opposed to trying to work your way through complicated internal systems.
MAP Monitoring
Minimum advertised price (MAP) monitoring is the standard practice to make sure a brand’s online prices are aligned with their pricing policy. With tons of resellers and distributors, it’s impossible to monitor the prices manually. That’s why web scraping comes in handy because you can keep an eye on your products’ prices without lifting a finger.
How can I extract data from a website for free?
There are various free scraping solutions available that allow you to automate the process of extracting data from the web. These range from simple point-and-click scraping solutions aimed at non-specialists to more powerful developer-focused applications with extensive configuration and management options.
If you’re viewing a website, just as you’re doing now, you could simply cut and paste the information you’re reading on screen into another document like a spreadsheet. It’s certainly one way of extracting web data for free. But gathering information manually this way is going to be slow, inefficient, and error-prone for all but the simplest tasks.
In practice you’ll be looking at ways to automate this process, allowing you to extract data from lots of web pages, maybe thousands or millions of them per day and organize the results in a neatly organized structure. To achieve this you’ll need some kind of web data extraction tool, often known as a web scraper.
There are plenty of free scraping solutions out there to extract data from the web. Some of these are dedicated applications aimed firmly at programmers, requiring a level of coding proficiency to configure and manage.
Ideal for non-specialists with moderate extraction needs, there are also some easy-to-use scrapers that run as a browser extension or plug-in with a simple point-and-click interface. Less sophisticated than their developer-focused counterparts, they’re typically more limited in the variety and volume of data they let you scrape.
Web Scraping Techniques
Most common techniques used for Web Scraping are
- Human copy-and-paste.
- Text pattern matching.
- HTTP programming.
- HTML parsing.
- DOM parsing.
- Vertical aggregation.
- Semantic annotation recognizing.
- Computer vision web-page analysis.
Human Copy-and-Paste
The simplest form of web scraping is manually copying and pasting data from a web page into a text file or spreadsheet. Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.
Text Pattern Matching
A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression-matching facilities of programming languages (for instance Perl or Python).
HTTP Programming
Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.
HTML Parsing
Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form, is called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme.[2] Moreover, some semi-structured data query languages, such as XQuery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.
DOM Parsing
Further information: Document Object Model, By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic content generated by client-side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages. Languages such as Xpath can be used to parse the resulting DOM tree.
Vertical Aggregation
There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no “man in the loop” (no direct human involvement), and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform’s robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor-intensive to harvest content from.
Semantic Annotation Recognizing
The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer, are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.
Computer Vision Web-Page Analysis
There are efforts using machine learning and computer vision that attempt to identify and extract information from web pages by interpreting pages visually as a human being might.
In a future article, I’ll go over advanced web scraping with an example.
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