Web scraping is practiced all the time online, but you may not acknowledge what it is about. The internet is a treasure trove of content and web scraping gives us the best tools to obtain valuable information from countless web pages. But, in order to do so, you will need a guide for web scraping.
What is Web Scraping?
Web scraping equips us with the means to download critical data from websites based on precise parameters. Today’s smart spiders do much of this work. These bots inspect websites and bring the necessary information to search engine databases. Therefore, web crawling is an indispensable component of web scraping.
The meaning and methods of web scraping are quite simple to grasp. First, there are web pages that meet specific criteria. The pages are then downloaded and retrieved for processing, where they are examined, reformatted, reproduced, etc. Web scrapers can obtain, amongst other things, images, videos, text, contact data, product articles, and more from a website.
How does Web Scraping work?
Nearly all web data scrapers are merely intelligent robots. In general, these scrapers are accountable for getting the HTML code of a web page and then organizing it into structured data.
First, a GET request is transmitted with an HTTP protocol to the targeted web page. The web server processes the request and, if accepted, the scraper is permitted to view and obtain the HTML of the website. A web scraper finds the targeted items and stores them in the set variables.
What is the applicability of Web Scraping?
- Scraping Amazon and other e-commerce and tracking the prices of competitors .
- Taking over the products with all the necessary information (images, description, meta-features).
- Obtaining databases related to different companies.
- Access to any information lost from websites that are no longer functional.
The Easy Way of Web Scraping
Let’s start with a guide for web scraping using the easy way to do it.
Find the URL you want to submit to web scraping. For example, if you’re studying the competitive prices of certain products, it might be clever to gather a list of all the websites that hold important data before you commence.
Examine the page and review the tags. The web scraper expects to be told what to do. So you need to find out carefully what items you will be looking at, as well as the labels. Inspect the backend of the website, including tags and metadata that will prove vital to your web scraper. Some web scrapers (i.e. Data Scraper Chrome Extension) want you to tell them exactly what tags do you want to be scrapped. Others (i.e. ParseHub) have a nice user-friendly interface and you don’t need to worry about examining the backend of the webpage.
Enable web scraping. You can write it from scratch in a programming language such as Python. Also, you can use software that simplifies this step. There are many out there, but most are not free. Companies usually use them as part of SaaS web data platforms. If you plan to do web scraping for just several websites, then we recommend that you build your own scraper.
Unpack your data. After allowing the web scraper to run awhile, you will have a fine selection of data waiting to be analyzed. You want to apply regular expressions (Regex) to transform it into regular text. This final step depends on the volume of data you have assembled, to determine if you require to consider further steps to properly analyze your findings.
Solutions for Automatic Data Collection
- Robot Framework is relatively easy to use. But the possible errors generated by the software does not attract developers’ responsibility for the obtained results. It is not a mature application tested by a large number of users.
- Scrapy Framework has high extensibility and scalability. It is a mature framework with many libraries and software as plugins. On the downside, it requires intermediate to advanced knowledge of Python programming language. Scalability requires appropriate hardware resources and it has a steep learning curve.
- Apache Nutch is a mature framework for big data. But it has a steep learning curve and requires the incorporation of several big data processing technologies like Hadoop, MapReduce, Solr, Spark.
- Rvest is an easy to use R library. It helps to integrate the results in R. On the downside, it has reduced scalability. The developers made it for small applications and familiarization with web scraping techniques.
It is necessary to develop a policy and operational procedures for the collection and use of data collected automatically from web pages as alternative data sources.
Why a Proxy is Crucial for Web Scraping?
Utilizing a proxy decreases the odds of a website’s anti-scraping tools to identify you and to expose and/or blacklist your crawler. The progress of your proxy will depend on certain circumstances. You must think about how often you assign requests, how you handle your proxies, and the type of proxies you are managing.
With dedicated proxies, you pay for obtaining a private pool of IPs. This can be a more reliable choice than a shared pool of IPs because you know which crawling projects have been conducted with these IPs. To be the sole manager of a dedicated pool of proxies is the most trustworthy, most efficient choice as you have full authority over what actions you conduct with the IP pool. Multiple proxy providers grant this as a built-in advantage in their residential proxies packages.
There is no reservation that working with a proxy service can solve problems and support you to succeed against anti-scraping measures. You have many options to employ for unblocking, crawling, and managing IPs individually.
I hope this guide for web scraping will be of use to you. Finally, the decision is yours and will depend on your web scraping necessities, resources, and technological demands.