【2023】 Top 10 Best Web Scraping Tools for Data Extraction. Categories of Data Extraction Tools

【2023】 Top 10 Best Web Scraping Tools for Data Extraction. Categories of Data Extraction Tools

In order to determine the best Data Extraction Tool for a company, the type of service the company provides and the purpose of Data Extraction is very important parameter. In order to understand this all the tools are categorized into 3 categories and are given below:

1) Batch Processing Tools

There are times when companies need to transfer data to another location but encounter challenges because such data are stored in obsolete forms, or are legacy data. In such cases moving the data in batches is the best solution. This would mean the sources may involve a single or few data units, and may not be too complex. Batch Processing can also be helpful when moving data within a premise or closed environment. To save time and minimize computing power, this can be done during off-work hours.

2) Open Source Tools

Open Source Data Extraction Tools are preferable when companies are working on a budget as they can acquire Open-Source applications to extract or replicate data provided. Company employees have the necessary skills and knowledge required to do this. Some paid vendors also offer limited versions of their products for free, therefore, this can be mentioned in the same bracket as Open-Source tools.

3) Cloud-Based Tools

Cloud-Based Data Extraction Tools are the predominant extraction products available today. They take away the stress of computing your logic and discard the security challenges of handling data yourself. They allow users to connect data sources and destinations directly without writing any code making it easy for anyone within your establishment to have quick access to the data which can then be used for analysis. There are several Cloud-Based tools available in the market today.

Scraping bot. Method 1: Using Selenium

We need to install ato automate using selenium, our task is to create a bot that will be continuously scraping the google news website and display all the headlines every 10mins.

Stepwise implementation:

Step 1:

The next step is to open the required website.

# path of the chromedriver we have just downloaded PATH=r"D:\chromedriver" driver=webdriver.Chrome(PATH)# to open the browser # url of google news website url=' https://news.google.com/topstories?hl=en-IN&gl=IN&ceid=IN:en ' # to open the url in the browser driver.get(url)

Output:

Step 3: Extracting the news title from the webpage, to extract a specific part of the page, we need its XPath, which can be accessed by right-clicking on the required element and selecting Inspect in the dropdown bar.

After clicking Inspect a window appears. From there, we have to copy the elements full XPath to access it:

Note: You might not always get the exact element that you want by inspecting (depends on the structure of the website), so you may have to surf the HTML code for a while to get the exact element you want. And now, just copy that path and paste that into your code. After running all these lines of code, you will get the title of the first heading printed on your terminal.

Python3

# Xpath you just copied news_path='/html/body/c-wiz/div/ div/main/c-wiz/div1>/div # to get that element link=driver.find_element_by_xpath(news_path) # to read the text from that element print(link.text)

Output:

‘Attack on Afghan territory’: Taliban on US airstrike that killed 2 ISIS-K men

Step 4: Now, the target is to get the X_Paths of all the headlines present.

One way is that we can copy all the XPaths of all the headlines (about 6 headlines will be there in google news every time) and we can fetch all those, but that method is not suited if there are a large number of things to be scrapped. So, the elegant way is to find the pattern of the XPaths of the titles which will make our tasks way easier and efficient. Below are the XPaths of all the headlines on the website, and let’s figure out the pattern.

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

So, by seeing these XPath’s, we can see that only the 5th div is changing (bolded ones). So based upon this, we can generate the XPaths of all the headlines. We will get all the titles from the page by accessing them with their XPath. So to extract all these, we have the code as

Now, the code is almost complete, the last thing we have to do is that the code should get headlines for every 10 mins. So we will run a while loop and sleep for 10 mins after getting all the headlines.

Scrapy. Crawler API ¶

The main entry point to Scrapy API is theobject, passed to extensions through theclass method. This object provides access to all Scrapy core components, and it’s the only way for extensions to access them and hook their functionality into Scrapy.

The Extension Manager is responsible for loading and keeping track of installed extensions and it’s configured through thesetting which contains a dictionary of all available extensions and their order similar to how you

subclass and aobject.

request_fingerprinter

The request fingerprint builder of this crawler.

This is used from extensions and middlewares to build short, unique identifiers for requests. See.

settings

The settings manager of this crawler.

For an introduction on Scrapy settings see.

For the API seeclass.

signals

The signals manager of this crawler.

For an introduction on signals see.

For the API seeclass.

stats

The stats collector of this crawler.

For an introduction on stats collection see.

For the API seeclass.

extensions

The extension manager that keeps track of enabled extensions.

Most extensions won’t need to access this attribute.

For an introduction on extensions and a list of available extensions on Scrapy see.

engine

The execution engine, which coordinates the core crawling logic between the scheduler, downloader and spiders.

Some extension may want to access the Scrapy engine, to inspect or modify the downloader and scheduler behaviour, although this is an advanced use and this API is not yet stable.

spider

Spider currently being crawled. This is an instance of the spider class provided while constructing the crawler, and it is created after the arguments given in themethod.

crawl ( * args , ** kwargs )

args and kwargs arguments, while setting the execution engine in motion.

Returns a deferred that is fired when the crawl is finished.

Web scraper cloud. A CLOUD BASED WEB SCRAPER

Introduction

In this article, we will guide you through the process of building a web scraper and setting it up to run autonomously on the cloud. It's important to understand what web scraping is before we delve into deployment. According to Wikipedia, web scraping is the process of extracting data from websites. There are various reasons one might want to extract data from a website, such as for analytical purposes or personal use. The use case will depend on the specific needs and goals of the individual or organization. If you're interested in learning more about web scraping, you can check out the Wikipedia article linked here:. It provides a comprehensive overview of the topic.

There are several techniques for web scraping that can be implemented using a variety of programming languages. In this article, we will be using the Python programming language. Don't worry if you're not familiar with Python, as we will be explaining each step in detail. If you do have a basic understanding of Python syntax, this should be a fairly easy process.

Our web scraper will be tasked with extracting news articles from a specific news website. The main reason for creating an autonomous web scraper is to extract data that is constantly being updated, such as news articles. This allows us to easily gather and analyze the latest information from a particular site. So, let's get started and build our web scraper!

Disclaimer: before scraping any website be sure to read their user terms and conditions. Some sites may take legal action if you don't follow usage guidelines.

Platforms and services

In this section, we will provide an overview of the platforms and services we will be using to create a cloud-based web scraper as an example. We will briefly explain the purpose and function of each platform or service to give you a better understanding of how they will be used in the process.

• IBM cloud platform: this will be our cloud platform of choice, for the reason being that you can access several services without having to provide credit card information. For our example we'll get to work with :
  • Cloud functions service: this service will allow us to execute our web scraper on the cloud.
  • Cloudant: a non-relational, distributed database service. We'll use this to store the data we scrape.
• Docker container platform: this platform we'll allow us to containerize our web scraper in a well defined environment with all necessary dependencies. This action allows our web scraper to work on any given platform that supports docker containers. In our example our docker container will be used by the ibm cloud functions service .
• Github: we'll use Github for version control and also to link to our docker container. Linking our docker container to a github repository containing our web scraper will automatically initiate a new build for our docker container image. The new image will carry all changes made to the repository's content.
• Cloud phantomjs platform: this platform will help render the web pages from the HTTP requests we'll make on the cloud. Once a page is rendered the response is returned as HTML.
• Rapid API platform: this platform will help manage our API calls to the cloud phantomjs platform and also provide an interface that shows execution statistics.

Octoparse Premium Pricing & Packaging

5 Day Money Back Guarantee on All Octoparse Plans

• All features in Free, plus:
• 100 tasks
• Run tasks with up to 6 concurrent cloud processes
• IP rotation
• Local boost mode
• 100+ preset task templates
• IP proxies
• CAPTCHA solving
• Image & file download
• Automatic export
• Task scheduling
• API access
• Standard support

Professional Plan

Ideal for medium-sized businesses

$249 / Month

when billed monthly
(OR $209/MO when billed annually) Buy Now Apply for Free Trial

• All features in Standard, plus:
• 250 tasks
• Up to 20 concurrent cloud processes
• Advanced API
• Auto backup data to cloud
• Priority support
• Task review & 1-on-1 training

Enterprise

For businesses with high capacity requirements

Enjoy all the Pro features, plus scalable concurrent processors, multi-role access, tailored onboarding, priority instant chat support, enterprise-level automation and integration

Contact Sales

Data Service

Starting from $399

Simply relax and leave the work to us. Our data team will meet with you to discuss your web crawling and data processing requirements.

Request a Quote

Crawler Service

Starting from $250

Enterprise

Starting from $4899 / Year

• For large scale data extraction and high-capacity Cloud solution.
• Get 70 million+ pages per year with 40+ concurrent Cloud processes. 4-hour advanced training with data experts and top priority.

Data Service

Starting from $399

• Simply relax and leave the work to us. Our data team will meet with you to discuss your web crawling and data processing requirements.

Sites for Scraping. 10 Best Web Scraping Tools for Digital Marketers

Data extraction and structurization is a commonly used process for marketers. However, it also requires a great amount of time and effort, and after a few days, the data can change, and all that amount of work will be irrelevant. That’s where web scraping tools come into play.

If you start googling web scraping tools, you will find hundreds of solutions: free and paid options, API and visual web scraping tools, desktop and cloud-based options; for SEO, price scraping, and many more. Such variety can be quite confusing.

We made this guide for the best web scraping tools to help you find what fits your needs best so that you can easily scrape information from any website for your marketing needs.

Quick Links

What Does a Web Scraper Do?

A web scraping tool is software that simplifies the process of data extraction from websites or advertising campaigns. Web scrapers use bots to extract structured data and content: first, they extract the underlying HTML code and then store data in a structured database as a CSC file, an Excel spreadsheet, SQL database, and other formats.

You can use web scraping tools in many ways; for example: 

• Perform keyword and PPC research.
• Analyze your competitors for SEO purposes.
• Collect competitors’ prices and special offers.
• Crawl social trends (mentions and hashtags).
• Extract emails from online business directories, for example, Yelp.
• Collect companies’ information.
• Scrape retailer websites for the best prices and discounts.
• Scrape jobs postings.

There are dozens of other ways of implementing web scraping features, but let’s focus on how marketers can profit from automated data collection. 

Web Scraping for Marketers

Web scraping can supercharge your marketing tactics in many ways, from finding leads to analyzing how people react to your brand on social media. Here are some ideas on how you can use these tools.

Web scraping for lead generation

If you need to extend your lead portfolio, you may want to contact people who fit your customer profile. For example, if you sell software for real estate agents, you need those agents’ email addresses and phone numbers. Of course, you can browse websites and collect their details manually, or you can save time and scrape them with a tool. 

A web scraper can automatically collect the information you need: name, phone number, website, email, location, city, zip code, etc. We recommend starting scraping with Yelp and Yellowpages. 

Now, you can build your email and phone lists to contact your prospects.

​​Web scraping for market research

With web scraping tools, you can scrape valuable data about your industry or market.For example, you can scrape data from marketplaces such as Amazon and collect valuable information, including product and delivery details, pricing, review scores, and more.

Using this data, you can generate insights into positioning and advertising your products effectively.

For example, if you sell smartphones, scrape data from a smartphone reseller catalog to develop your pricing, shipment conditions, etc. Additionally, by analyzing consumers’ reviews, you can understand how to position your products and your business in general.

​​​​Web scraping for competitor research

You may browse through your competitors’ websites and gather information manually, but what if there are dozens of them that each have hundreds or thousands of web pages? Web scraping will save you a lot of time, and with regular scraping, you will always be up-to-date.

You can regularly scrape entire websites, including product catalogs, pricing, reviews, blog posts, and more, to make sure you are riding the wave.

Web scraping can be incredibly useful for PPC marketers to get an insight into competitors’ advertising activities. You can scrape competitors’ Search, Image, Display, and HTML ads. You’ll get all of the URLs, headlines, texts, images, country, popularity, and more in just a few minutes.

​​​​Web scraping for knowing your audience

Knowing what your audience thinks and what they talk about is priceless. That’s how you can understand their issues, values, and desires to create new ideas and develop existing products. 

Web scraping tools can help here too. For example, you can scrape trending topics, hashtags, location, and personal profiles of your followers to get more information about your ideal customer personas, including their interests and what they care and talk about. You may also create a profile network to market to specific audience segments.

Web scraping for SEO

Web scraping is widely used for SEO purposes. Here are some ideas about what you can do:

• Analyze robots.txt и sitemap.xml.

Data Scraping. Description

Normally, data transfer between programs is accomplished using data structures suited for automated processing by computers , not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed , and minimize ambiguity. Very often, these transmissions are not human-readable at all.

Thus, the key element that distinguishes data scraping from regular parsing is that the output being scraped is intended for display to an end-user , rather than as an input to another program. It is therefore usually neither documented nor structured for convenient parsing. Data scraping often involves ignoring binary data (usually images or multimedia data), display formatting, redundant labels, superfluous commentary, and other information which is either irrelevant or hinders automated processing.

Data scraping is most often done either to interface to a legacy system , which has no other mechanism which is compatible with current hardware , or to interface to a third-party system which does not provide a more convenient API . In the second case, the operator of the third-party system will often see screen scraping as unwanted, due to reasons such as increased system load , the loss of advertisement revenue , or the loss of control of the information content.

Data scraping is generally considered an ad hoc , inelegant technique, often used only as a "last resort" when no other mechanism for data interchange is available. Aside from the higher programming and processing overhead, output displays intended for human consumption often change structure frequently. Humans can cope with this easily, but a computer program will fail. Depending on the quality and the extent of error handling logic present in the computer, this failure can result in error messages, corrupted output or even program crashes .