4 Best e-Commerce Scrapers in 2022: How to Scrape Store Data With Python

Last Updated: October 5, 2022
If you’re interested in gathering data from e-commerce sites, the best solution is to do web scraping. Let’s take a look at the best e-Commerce scrapers options right now.
Best e-Commerce Scrapers
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The best e-Commerce scraper in 2022, as found in our independent testing, is ScraperAPI!


The e-Commerce market is a highly competitive space and simply selling good quality products with efficient service isn’t enough.

The work you put into marketing and product development can really make or break your business‌. 

To excel at these tasks, you need to amass data from your competitors, learn industry best practices, read about product utility and reviews and predict future trends in sales.

The best way to do this is by creating a robust system of information comprising datasets that focus on both obvious vital aspects and small details of competitors and their services. 

To achieve this, you can always go online and look at your competitors’ websites or shops at marketplaces like Amazon or Alibaba, but there’s no cut-out way of obtaining datasets with relevant information.

So if you’re interested in gathering data from e-commerce sites, the best solution is to do web scraping.

You can extract data through web-scraping in two ways. The first way is using automated data scraping tools. This is the easier but often costlier route.

The second way is running code in Selenium using a coding language like Python or C++. This is better suited for experienced programmers only. 

Because there are so many e-Commerce scraping tools, finding the right software for your needs can be difficult.

So to help you out, we’ve listed the most efficient e-Commerce scrapers below, along with Python code you can run in BeautifulSoup if you’re not a fan of automated coding apps. Let’s dive in!

Best e-Commerce Scraper 2022

Here’s a quick look at the best e-Commerce scrapers for scraping website data:

  1. ScraperAPI – 🏆 Winner!
  2. Bright Data
  3. Apify
  4. Octoparse

1. ScraperAPI – #1 e-Commerce Scraper

ScraperAPI

ScraperAPI is an online e-commerce scraper tool that can be used to extract data from e-commerce sites.

It provides a great deal of functions like captcha solving, residential rotating proxies, built-in drivers, etc.

The extension runs on all browsers and facilitates JavaScript rendering like Python Selenium codes.

With ScraperAPI, you don’t have to fetch any webpages either- your sole focus has to be data processing and management.

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And because of the rotating residential proxies, there’s no risk of you getting banned by websites and losing any progress. 

ScraperAPI also offers sticky proxies if there’s a website with less strict protocols.

You also have the feature to customise your geo-location based on region restrictions and customise request headers for parsing in-app data.

ScraperAPI is malleable and developers can customise it with their own codes using JavaScript and Python.

ScraperAPI lets you render JavaScript to using a simple & render=true code. 

The app offers users 5000 APIs free. After that, you’ll have to subscribe to a paid plan. These begin at $29/mo, which is significantly cheaper than competitors.

2. Bright Data

Bright Data Data Collector

Our next pick e-Commerce scraper is Bright Data. Formerly known as Luminati, Bright Data is a leading proxy provider that offers various exclusive features and services.

You can run the software’s own data collector, or use the Web Scraper extension and run the app as a proxy. 

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Moving on, when it comes to Bright Data, there are many positives and important features to talk about, so let’s get started.

First of all, using Bright Data requires absolutely zero coding knowledge- the data collection process is simplified and you can retrieve any information about sellers or their products through marketplaces.

The data collector is a patented, peer network technology that can go beyond roadblocks displayed by websites for visitors.

Gathering this data is effortless and helps you uncover a lot of crucial information like current pricing, listing price, discounts, and selling price.

You can compare the prices of identical products, apply filters for different models, and get an average listing price for a group of items. Bright Data will alert you to new promotions and sales by sellers who you’re crawling.

Bright Data is excellent for businesses in other ways too.

For example, they offer development resources and have developers and product managers standing by to provide support when needed.

Bright Data product management includes real-time product discovery, item matching, discovering new categories, making product profiles, etc.

You also get updates once the suppliers you’re scraping list new items, as well as spot shifts that focus on determining which product has increasing popularity and which product isn’t selling.

With this data, you can also discover the gaps in your store and inventory that can be exploited. 

Running the Bright Data data collector is easy- you just need to pick what websites you want information from, then pick the frequency of how often you want data updated (real-time, scheduled, one-time, etc).

Afterward, you get to choose the best delivery format for your file. Bright Data offers JSON, HTML, CSV, and Excel.

Finally, just select where you want the data delivered and stored (email, webhook, Cloud, Drive, Microsoft Azure, API, or SFTP), and you’re done!

The automated data collection will begin.

A wonderful feature about this e-Commerce scraper is the vast number of templates to pick from- if you want to write your own code, you don’t have to start from scratch.

There are templates for Amazon product search, Alibaba, eBay and more.

Once you sign up for the paid plan, you also get access to their JavaScript Development Environment, where you can edit commands for collecting info.

There’s no set limit for the number of sites you can crawl, and the data you store can have unlimited volume.

The collector has a point-and-click interface and runs on AI that bypasses security set up by public marketplaces.

Beyond these nifty features, where Bright Data shines is the massive proxy pool it provides.

No proxy service comes close to the huge 72 million IPs Bright Data provides as residential proxies

On top of that, if you want to run datacenter proxies on a site that doesn’t block you from scraping like bots, you get 7 million IPs.

Bright Data also has mobile proxies comprising more than 2 million IPs as well. The proxies are worldwide- US alone features 4.6 million of these IPs. 

Because of the high number of proxies, their geo-targeting is brilliant and you can easily access sites that are only available in certain regions (like Costco) without a VPN.

You can also use your ASN during geo-targeting for more control over the city or country chosen.

As for security, Bright Fata uses the HTTPS protocols and all the 81 million proxies are encrypted. No third party can retrieve your info.

Plus, the IP addresses are legal and transparent. Bright Data has a whopping 93.7% success rate with data scraping, and that includes the popular and tightly-regulated sites like Alibaba, Amazon, Google, etc.

To give you a quick review of everything we talked about above, Bright Data is an excellent proxy provider with data collecting tools that don’t require coding knowledge.

Pros include ASN targeting, legal scraping, excellent customer service, fast residential proxies with large IP pool, geo-targeting, secure login, next-gen proprietary tech, and strict KYC.

You also get a 7-day trial with a 3-day money refund policy.

3. Apify

Apify

Apify is another brilliant e-commerce scraper that boasts tons of features, allowing you to extract data without writing a single line of code.

However, the software is pretty flexible and does allow you to run your own codes.

Apify has special tools for scraping e-commerce sites like Amazon, AliExpress, eBay, and more available on the store.

These are created by developers and work with Api proxies. Their costs vary, for example the Amazon scraper costs $60.

However, you get 20,000 free results a month with the Free plan, after which it’s $5 per 20k results.

We’ll take the example of Amazon Product Scraper here. It’s a tool that enables you to retrieve product data by Amazon URLs.

You simply have to enter the web address in the input field and pick the maximum number of products you want to be scrapped.

Choose your desired fields then simply download all the extracted data from the Dataset tab.

The benefits of Amazon Product Scraper include monitoring categories and subcategories, discovering popular and up and coming products and brands, note reviews, fine-tuning of advertisements, etc.

These transform your web analytics.

Moving on to the Apify software itself, the proxy tool has a user-friendly and simple interface.

You might feel a small learning curve when you start using Apify but you’ll find tons of online resources on the app’s official site, which includes an active developers community that can aid you in scraping e-commerce sites. 

Furthermore, if you’re a business looking for a permanent scraping solution, you can buy a turnkey project from an Apify certified developer too.

You can join Apify for free with its Free plan, but it’s only good for understanding how the software works.

We recommend opting for the paid plans- Personal ($45/mo) is good for individuals and Team ($499/mo) is suited for small to midsize businesses.

Joining paid plans gets you essential features like better data retention, greater support, active RAM and greater number of data-shared proxies.

The Apify e-commerce scraper  has an API that extracts product data, selling price, reviews, changes in popularity, etc.

There’s an excellent in-app translator for several languages so you can receive accurate product details and description.

And when it comes to downloading extracted data, Apify provides several format options that include HTML, table, JSON, CSV, XML, RSS feed, and Excel.

The Apify API integrates with Zapier and Integromat seamlessly. There are rotating proxies initiated by the smart AI too, which save you from getting blocked on sites you’re scraping.

The proxy bots perform functions similar to human activity, which minimises the chances of your account being blacklisted.

Lastly, since Apify runs on open-source codes and tools, you don’t have to worry about vendor lock-in.

Apify as a whole can be used as an API to connect to a particular software.

4. Octoparse

Octoparse

Our final pick for e-commerce scraper is Octoparse. This is a popular scraping program for all sorts of data extraction and it’s packed with well-rounded tools and features.

The best part of using Octoparse is you don’t need to know code- every scraping task has a template and you can add filters and make changes easily.

Octoparse also has an active blog and website that hosts tutorials and guides for using the app.

Still, if you’re interested in running your own code, Octoparse more than welcomes it. The software has a simple interface that runs on a point and click mechanism.

You get to export data gathered in multiple formats that include TXT, HTML, JSOV, CSV, and Excel. It’s compatible with Zapie and Google Sheets.

Your collected data can be uploaded to MySQL, SQL Server, and Oracle. Other notable features include VPN, residential proxies, captcha solver, automatic scrolling, etc.

With Octoparse’s e-Commerce scraper tool, you can fetch prices from all accessible and location-restricted sites online within a matter of minutes, helping you match prices and stay ahead of the competition.

You can also track bestseller rankings, scrape product data, learn about inventory/stock availability, collect reviews, and monitor SKUs. You can identify MAP violations instantly.

Octoparse has a freemium pricing plan with a 14-day trial. Subscriptions start at a measly $75/mo.

This includes an ad-block, which helps increase scraping speed and lowers the time taken to load a new URL.

Lastly, let’s get down to using Octoparse and its auto-detection algorithms for scraping.

Our guide is a no-code one, but you can follow the steps and build a crawler if you want. If you want to practise some more, you can work on the Octoparse HelloWorld site.

Most e-commerce websites run similar layouts- a home page with search, then a webpage with items listed, then a product page for each item.

Octoparse auto-detection is designed to extract product data from the nested lists.

It moves on to detect links and text elements alike, then generates a scraping command. If there are several pages, the Next Page and Load More buttons will be clicked automatically.

To begin extracting data, first create a new task. Enter the URL you want to scrape in the search box and hit Start.

Octoparse loads the URL you posted in a built-in browser window. The auto-detect process will start now.

This takes a while. Once done, you can see the additional info and instructions in the Tips panel.

Click on these Tips and skim through the preview section data. Here, you can rename any data fields in your database and remove the ones you don’t need.

All the detected data will be highlighted on the browser site.

After you’re done picking the right fields, go back to Tips and check the options available. These change depending on the data retrieved. 

For example, if you scrape a list URL, the data detected will give you a default option of scraping the current list.

You have other options you can add to it, like allowing the app to click on the Next button itself so data from multiple pages is sourced.

There may be options for other buttons present on the page too.

To make sure you’ve selected the right button, click on Check- this will highlight your chosen options on the webpage.

Another likely option would be Links, which captures data on the following pages and the pages whose links are detected by the software.

You can check this option similar to choosing the Next Page and filter buttons.

Since most scrapers want to crawl information on all the pages, you can go ahead and select all three available options. Once done, you can save the task settings.

You can run the task immediately or wait and edit the workflow manually. There’s no time restriction on running Octoparse for data.

How To Scrape e-Commerce Store Data Using Python

This part of the article is meant for coders and developers. If you’re not a programmer and don’t know how to write code it’s best for you to stick to the automated e-Commerce scraper tools above.

Now, as a coder, you’re probably well aware that developing a scraper for e-commerce isn’t too hard.

e-Commerce scrapers are just bots, and you just have to parse required data by sending requests.

Because of this, you can use your preferred coding language. However, we’re going to use Python for discussion and examples. Python is the best for beginners.

Another thing to keep in mind is that every e-commerce site is different, so it’s hard to say what tool works the best definitively.

But for the most part, e-commerce sites like Amazon use JavaScript. 

Now whether you use Selenium or BeautifulSoup+Request depends on the site. If you have to render the JavaScript after extracting, then Selenium is the right tool.

Selenium works best on JavaScript heavy websites, but makes the rendering process painfully slow. 

The combo of BeautifulSoup and Requests is ideal for sites that don’t need to render with JavaScript on, such as the Amazon product pages.

You can use Scrapy along with the two for a better experience. This is the combo we’re using today.

Remember when writing code yourself that most platforms consider these scraping bots spam.

There are always measures to stop scraper action and protocols are in place most of the time. 

If you’re unsure whether or not a website will block you after scraping, check the robots.txt file. We’ve mentioned the method to do so later in this article, so keep reading. 

To help you get started with scraping e-commerce sites, we’ve written a basic sample code.

The code targets Amazon and can be used to retrieve information from products.

It takes the ASIN of the product you’re crawling on the site, then gives you its details like rating, name, price, tags, variety, etc.

We’re using a duo of Requests and BeautifulSoup since Amazon isn’t a JavaScript-heavy site and doesn’t depend on rendering.

Since this is a simple script that only has commands for copying product data, it doesn’t handle any exceptions or filters.

It doesn’t integrate any VPN or proxies either, and since Amazon runs on anti-scraping protocols, your IP will get blocked after a couple of attempts.

import requests

from bs4 import BeautifulSoup

user_agent = ‘Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko)Chrome/80.0.3987.132 Safari/537.36’

accept = “text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9”

accept_en = “gzip, deflate, br”

accept_lan = “en-US,en;q=0.9”

cache_con = “max-age=0”

cokies = “”

down_link = “0.35”

headers = {‘accept’: accept,

           ‘accept-encoding’: accept_en,

           ‘accept-language’: accept_lan,

           ‘cache-control’: cache_con,

           ‘cache’: cokies,

           ‘user-agent’: user_agent,}

class AmazonProductScraper:

    def __init__(self, asin):

        self.asin = asin

        self.page_url = “https://www.amazon.com/dp/” + self.asin

    def scrape_product_details(self):

        content = requests.get(self.page_url, headers=headers)

        soup = BeautifulSoup(content.text, “html.parser”)

        product_name = soup.select(“#productTitle”)[0].text.replace(“\n”, “”)

        product_price = soup.find(“span”, {“class”: “a-price”}).find(“span”).text

        product_review_count = soup.find(“span”, {“id”: “acrCustomerReviewText”}).text.replace(“ratings”, “”).strip()

        product_categories = []

        for i in soup.select(“#wayfinding-breadcrumbs_container ul.a-unordered-list”)[0].findAll(“li”):

            product_categories.append(i.text.strip())

            product_details = {‘name’: product_name,

                               “price”: product_price,

                               “categories”: product_categories,

                               “review_count”: product_review_count}

            print(product_details)

            return product_details

product_asin = “B075FGMYPM”

x = AmazonProductScraper(product_asin)

x.scrape_product_details()

Is Scraping Data From A Website Legal?

There are a couple of ways to check whether or not the website you’re about to scrape allows you to do so legally.

If it doesn’t, you must use a proxy and/or VPN to avoid getting permanently banned.

The first method is simple- you need to read the robots.txt file. This is a readable file that’s usually found at the website domain as /robots.txt. Open this file.

All the bots listed by ‘user-agent’ are disallowed and you’re not permitted to scrape the site.

To find this file, simply type in the URL then add slash a and write robots.txt.

The second method is running a Python code in BeautifulSoup + Requests. This status_code is written here:

import requests

from bs4 import BeautifulSoup 

r=requests.get(” ENTER URL OF YOUR CHOICE”)

r.status_code

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Written by Jason Wise

Hi! I’m Jason. I tend to gravitate towards business and technology topics, with a deep interest in social media, privacy and crypto. I enjoy testing and reviewing products, so you’ll see a lot of that from me here on EarthWeb.
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