Comparison of popular Web scraping API services. What to consider when scraping the Web?

Comparison of popular Web scraping API services. What to consider when scraping the Web?

• Scraping Intervals - how often do you need to extract information? Is it a one-off thing? Should it happen regularly on a schedule? Once a week? Every day? Every hour? Maybe continuously?
• Data Input - what kind of data are you going to scrape? HTML, JSON, XML, something binary, like DOCX - or maybe even media, such as video, audio, or images?
• Data Export - how do you wish to receive the data? In its original raw format? Pre-processed, maybe sorted or filtered or already aggregated? Do you need a particular output format, such as CSV, JSON, XML, or maybe even imported into a database or API?
• Data Volume - how much data are you going to extract? Will it be a couple of bytes or kilobytes or are we talking about giga- and terabytes?
• Scraping Scope - do you need to scrape only a couple of pre-set pages or do you need to scrape most or all of the site? This part may also determine whether and how you need to crawl the site for new links.

Python scraping api. Understanding Web Scraping with Python

But first, what does web scraping mean? On the most basic level, a web scraper extracts the data from a website, provided that not all of them offer their data under a public API.

This process is more useful than it seems if you consider that the more information you have, the better decisions you take in your business.

Nowadays, websites are more and more content loaded, so performing this process entirely by hand is far from a good idea. That is where building an automated tool for scraping comes into the discussion.

“What do I need the data for?” you may ask. Well, let’s have a look at some of the top use cases where web scraping is a lifesaver:

• Price intelligence : an e-commerce company will need information about competitors’ prices to make better pricing and marketing decisions.
• Market research : market analysis means high quality, high volume, and insightful information.
• Real estate : individuals or businesses need to aggregate offers from multiple sources.
• Lead generation : finding clients for your on-growing business.
• Brand monitoring : companies will analyze forums, social media platforms, and reviews to track how their brand is perceived.
• Minimum advertised price (MAP) monitoring ensures that a brand’s online prices correspond with its pricing policy.
• Machine learning : developers need to provide training data for their AI-powered solutions to function correctly.

You can find more use cases and a more detailed description of them here.

“Cool, let’s get it started!” you may say. Not so fast.

Even if you figure out how web scraping works and how it can improve your business, it’s not so easy to build a web scraper. For starters, some people don’t want a scraper on their websites for different reasons.

One of them would be that scraping means many requests are sent in a second, which can overload the server. Website owners can consider this sometimes as a hacker’s attack ( denial of service ), so websites adopt measures to protect themselves by blocking the bots.

Some of these measures can be:

• IP blocking : this happens when a website detects a high number of requests from the same IP address; the website can ban you entirely from accessing it or significantly slow you down).
• CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart): are logical problems pretty trivial to solve for people but a headache for scrappers.
• Honeypot : integrated links invisible to humans but visible to bots; once they fall into the trap, the website blocks their IP.
• Login required : websites may hide some information you need behind a login page; even if you authenticate on the website, the scraper does not have access to your credentials or browser cookies.

Some websites may not implement these techniques, but the simple fact that they want a better user experience using Javascript makes a web scraper’s life harder.

When a website uses Javascript or an HTML-generation framework, some of the content is accessible only after some interactions with the website are made or after executing a script (usually written in Javascript) that generates the HTML document.

Let’s also consider the quality of the data extracted. For example, on an e-commerce website, you may see different prices according to the region where you live. This data is not very accurate, so the bot must find a way to extract the data as accurately as possible.

If you manage to overcome all these, you still need to consider that website’s structure can always suffer changes. After all, a website needs to be user-friendly, not bot-friendly, so our automated tool must adapt to these changes.

In this never-ending scraping war , bots come up with solutions on their own. The scope of all of them is to recreate human behavior on the internet as best as possible.

You can integrate CAPTCHA solvers as well. They will help you achieve continuous data feeds but will slightly slow down the process of scraping.

As a solution to honeypot traps, you can use XPath (or even regular expressions if you are bold enough) to scrape specified items instead of the whole HTML document.

Considering all these issues and how to overcome them can become a painstaking and time-consuming process. That is why in the last decade, web scraping APIs gained more and more attention.

Here, on WebScrapingAPI, we collect the HTML content from any website, managing any possible challenge (like the ones mentioned earlier). Also, we use Amazon Web Services, so speed and scalability are not a problem. Are you tempted to give it a try? You can start with a free account , which offers you 1000 API calls per month. Dope, right?

Python Scraping API: Understanding Web Scraping with Python

Before we dive into the world of web scraping with Python, let's take a step back and understand what web scraping means. On the most basic level, a web scraper extracts data from a website, provided that not all of them offer their data under a public API.

This process is more useful than it seems if you consider that the more information you have, the better decisions you take in your business. Nowadays, websites are more and more content-loaded, so performing this process entirely by hand is far from a good idea. That is where building an automated tool for scraping comes into the discussion.

“What do I need the data for?” you may ask. Well, let's have a look at some of the top use cases where web scraping is a lifesaver:

• You can find more use cases and a more detailed description of them here.

“Cool, let's get it started!” you may say. Not so fast.

Even if you figure out how web scraping works and how it can improve your business, it's not so easy to build a web scraper. For starters, some people don't want a scraper on their websites for different reasons.

One of them would be that scraping means many requests are sent in a second, which can overload the server. Website owners can consider this sometimes as a hacker's attack (denial of service), so websites adopt measures to protect themselves by blocking the bots.

Some of these measures can be:

• Using CAPTCHAs to verify human interaction
• Implementing rate limiting to control the number of requests
• Using JavaScript to load content dynamically

Some websites may not implement these techniques, but the simple fact that they want a better user experience using JavaScript makes a web scraper's life harder.