In a previous post we’ve considered the ways to disguise an automated Chrome browser by spoofing some of its parameters – Headless Chrome detection and anti-detection. Here we’ll share the practical results of Fingerprints testing against a benchmark for both human-operated and automated Chrome browsers.
Imperva (that includes the former Distil anti-bot management) is a service providing many kinds of website protections. The present Imperva services include the following ones:
- Cloud Web Application Firewall (WAF)
- Bot Protection service (formerly Distil Networks)
- IP Reputation Intelligence
- Content Delivery Network (CDN)
- Attack Analytics solution (eg. DDoS)
As to the protection of the bot scraping activities we mention the following.
My goal was to retrieve data from a web business directory.
Since the business directories scrape is the most challenging task (beside SERP scrape) there are some basic questions for me to answer:
- Is there any scrape protection set at that site?
- How much data is in that web business directory?
- What kind of queries can I run to find all the directory’s items?
- Online marketplaces
In the marketplaces people offer their products for sale. Similar to garage sales, but online. (eg. eCrater, www.1188.no).
Easy to scrape since they are usually free and do not tend to protect their data.
- Business directories
The usually huge online directories targeted at the general audience. (eg. Yellow Pages). They do protect their data to avoid duplication and loss of audience. See some posts on this.
In this post I want to let you how I’ve managed to complete the challenge of scraping a site with Google Apps Script (GAS).
Most of the answers to the question in internet forums are given by services that automatically solve captchas. They provide services to solve CAPTCHA rather than to fully skip it.
Recently I received this question: What are the best online resources to acquire data from?
The top sites for data scrape are data aggregators. Why are they top in data extraction?
They are top because they provide the fullest, most comprehensive data [sets]. The data in them are highly categorized. Therefore you do not need to crawl and fetch other resources and then combine multiple-resource data.
Those sites fall into 2 categories:
- Goods and services aggregators. Eg. AliExpress, Amazon, Craiglist.
- Personal data and companies data aggregators. Eg. Linkedin, Xing, YellowPages. For such aggregators another name is business directories.
The first category of sites and services is quite wide-spread. These sites and services promote their goods with the goal of being well-known online, to have as many backlinks as possible to them.
The second category, the business directories, does not tend to reveal its data to the public. These directories rather promote their brand and give scraping bots minimum opportunity for data acquiring*.
Consider the following picture where a company’s data aggregator gives to the user only 2 input fields: what and where.
You can find more of how to scrape data aggregators in this post.
*You have to adhere to the ToS of each particular website/web service when you perform its data scraping.
Recently I’ve got a question:
How do I get pass the dynamic “load more” button using a Python web scraper?
We’ve already introduced you to the theory behind the new NO CAPTCHA reCAPTCHA v2, but now we come to the practical integration part. Here we’ll share how to insert and configure “NO CAPTCHA reCAPTCHA” into a web page.