Top 10 Best Web Scraping Software Services

Web scraping software

Web scraping can have a multitude of benefits for a business. Once you’ve clued yourself up on how it works and are ready to get going with your web scraping project, the next step is deciding which software tools to use.

This article rounds up Datamam experts’ top 10 free and open-source tools to help you decide on the best tool for you, as well as the pros and cons of each, and advice from experts on how to bring your scraping project to life.

Datamam’s Top 10 Best Web Scraping Tools

At Datamam, the global specialist data extraction company, we’ve pulled together their list of the top 10 web scraping tools, based on our experience of working with them. We’re really keen on keeping web scraping projects as simple as possible, so we’ve pulled out some of the most easy to use, cost effective, and efficient tools we know, that have great support available.

  1. Requests
  2. Selenium
  3. Puppeteer
  4. BeautifulSoup
  5. Lxml
  6. Regex (Regular Expressions)
  7. Pandas
  8. SQL
  9. Apache Spark
  10. Datamam

What are the main types of website scraping software?

Our list of top 10 best web scraping tools according to Datamam fall into each of these three main types of scraping tools, each of which can be most appropriate for different business needs and preferences.

Open-source tools are used to extract information from large datasets. These have few constraints in sharing, and there is room for tailoring for each project. Tools such as BeautifulSoup and Selenium fall into this category. Open-source tools are generally free to use, flexible and provide access to their source code, increasing transparency. However, they are often quite complex to use, and there is little official support available.

Free or Freemium commercial tools are free web scrapers provided under proprietary licenses, run by specialist providers. Some examples of free commercial tools are ScrapingBot and These tools are cost-effective, and quick and easy to set up and use, with an intuitive user interface. However, often free commercial web scrapers are often a free version of paid-for software, and come with limited features.

Bespoke tools are custom-developed solutions tailored to specific data extraction needs. These tools are designed to meet unique requirements that may not be fully addressed by standard solutions. Datamam is an example of a bespoke tool, a high-performance solution which gives organizations precise control over the functionality, features, and integration capabilities. Considerations include the associated costs which could grow depending on the complexity and scale of the project.

What are some of the considerations when choosing web scraping software?

There are a number of important things to consider when choosing software for a web scraping project. Each has its own pros and cons, and the most appropriate tool for the job really depends on case by case requirements. It also depends on which programming language you choose to use for the project, whether that’s Python, Javascript or HTML.

Some of the most important considerations are explored below:

  • Scraping frequency and intervals: Consider the scalability of the software to handle large volumes of data and scale up as your scraping needs grow. Software should be able to scrape data from multiple websites without losing efficiency.
  • The type of data needed: The types of data needed will dictate whether software is needed that can scrape structured or unstructured data. Unstructured data is generally more difficult to extract, and will necessitate a more powerful tool to extract and parse (or translate) into usable data.
  • The accuracy of the data: The bigger the volume of the data required, the more powerful the tool will need to be to maintain the accuracy and reliability of the data. Businesses should look for features in the tool such as data validation and error handling, which can help increase accuracy.
  • The complexity of sites being scraped: All websites are not created equal, and some will have more types of data and more anti-scraping techniques in place. A web scraping tool should be able to deal with more complex web sites, to enable you to get the data you require.
  • Captcha and other anti-scrape techniques: Scraping tools will have differing capabilities when it comes to dealing with IP blocking, and it is necessary to check these to make sure the desired data is collected. Meanwhile, web scraping software must comply with legal and ethical guidelines, including website terms of service and data privacy regulations
  • In-house expertise, resources and effort required: User-friendliness can be overlooked as a feature, but the simpler the tool is to use the better. Intuitive features can reduce the amount of in-house resource spent by making it easier to manage tasks without needing external support.
  • Integration into workflow: An important consideration for most businesses is how much time and money the tool can save over the course of the project. Each tool will have upfront costs, fees and sometimes additional rates for extra features, and businesses should align the cost with the size of the project.

Depending on the web scraping project, the best choice of software will vary. For smaller projects a simpler tool might do the trick, but for larger-scale, complex projects investing in a bespoke solution can improve accuracy and reliability, whilst keeping the time and resource spent in-house to a minimum.

A deeper dive into the Top 10 Tools

Running through the criteria in the section above will help you to shape your decision when choosing the right software for your web scraping project. The Datamam Top 10 web scraping tools have been chosen by experts at the company, and generally fall into four different categories:

  • Data Collection Tools: Gathers data from the web, capable of handling both simple requests and complex, interactive web pages.
  • Parsing Tools: Essential for extracting information from web content, converting unstructured data into an accessible and manipulable format.
  • Data Structurization Tools: After collecting and parsing the data, these tools help organize, clean, and prepare data for analysis or storage.
  • All-rounder Tool: Able to take a web scraping project from beginning to end.

Each of the scrapers has its own positives and negatives. Here we’ll look at Datamam’s chosen Top 10 best web scraping tools in a little more detail.

1. Requests

Largely billed as a data collection tool, Requests is a Python library for making HTTP requests easier and more streamlined. It has an efficient API specialized for sending requests (hence the name!) and managing responses.

Requests is very easy to use, requires minimal code to fetch data, and works well for static content. It is also relatively cheaper and faster than other browser-based libraries. However, it has its limitations as it is suitable only for retrieving raw HTML data, and does not process JavaScript which is often used for more dynamic factors such as images. It also cannot send requests asynchronously.


2. Selenium

An open-source data collection tool that acts as a framework for automating web browsers, allowing businesses to perform complex interactions with web pages. Programming languages like Java, Python, and C# are used with Selenium, which automates interactions such as filling in forms and clicking buttons.

Selenium can handle dynamic content loaded with JavaScript, and it supports multiple browsers (such as Opera, Mozilla Firefox, Microsoft Edge, Google Chrome, and Chromium) and programming languages. It is used widely in web scraping projects, and is known for its reliability and functionality. However, Selenium can be slow, and it requires a complex setup for web automation tasks which can need substantial human and tech resources.


3. Puppeteer

Puppeteer is a data collection tool with a Node.js library to control headless Chrome or Chromium, which is primarily used for web testing and scraping of complex sites. It automates web scraping, screenshot capture and pdf generation among other tasks, and is often used to test frameworks.

The tool’s easy-to-use API efficiently handles JavaScript, which is useful for automated testing and scraping dynamic content. Relatively faster than Selenium, it can be useful for mobile automation as well. However, Node.js specific, mostly supports only Chrome and Chromium, so it is not as straightforward for non-JavaScript environments.


4. BeautifulSoup

A Python library for parsing HTML and XML documents, BeautifulSoup extracts the relevant data and turns them into parseable trees. It is often used alongside data collection tools to streamline the web scraping process.

One of the most popular parsing tools, BeautifulSoup is user-friendly, great for beginners and handling malformed markup. However, it is not as fast as some other parsers, as it relies on external parsers like lxml for speed.


5. lxml

A high-performance, easy-to-use library that provides safe and convenient access to XML and HTML within Python. It offers ElementTree and XPath interfaces, to improve the process of navigating the document tree.

Lxml is very fast, highly efficient for parsing large files, and it can directly access XML and HTML trees. However, it can be less intuitive for beginners compared to BeautifulSoup and other parsers. It is also more strict in handling errors.


6. Regex (Regular Expressions)

A parsing tool that methodically matches patterns in text, Regex is ubiquitous in programming languages for searching and manipulating strings. It is possible to search for specific patterns with Regex, then extract and parse the relevant data from there. Regex is supported in a number of programming languages including Python and JavaScript.

The tool is extremely powerful for complex pattern matching, and it is language-agnostic which is essential for high-quality scraping. It does, however, have a steep learning curve, and it is easy to create inefficient or faulty expressions as it is not specific to HTML or XML structure.


7. Pandas

Pandas is a powerful Python library used for Data Structurization. It can manipulate and analyze data by providing flexible data structures. It can clean, transform and aggregate data efficiently, and can integrate with other Python libraries well to summarize datasets. Pandas can load data from a range of file formats.

Pandas has extensive functionality for data manipulation, wide support for different file formats, and integrated handling of missing data. The tool can offer intuitive and easy to use data structures, such as DataFrame, to work with structured data. However, it can be memory-intensive, and the more complex a project becomes the more it relies on expertise in web scraping – there is a steep learning curve.


8. SQL (Structured Query Language)

SQL is a domain-specific language used in programming for managing and manipulating relational databases. It can interact with a range of databases to enable data querying, and updating records. Its syntax is consistent across different database systems, ensuring portability and widespread applicability.

It is a universal tool which can be used for data querying and manipulation, and there is extensive support across database platforms. There are various flavors of SQL specific to different database management systems, including MySQL, Oracle, PostgreSQL, and SQL Server. This system is optimized for storage, offers high stability, and is well-suited for handling large-scale historical data. The tool also optimizes data access. However, it does require a solid understanding of database schema, and is not suitable for unstructured data without preprocessing.


9. Apache Spark

Apache Spark is an open-source unified analytics engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning, and graph processing. Its API supports Java, Scala, Python, and R, and it can offer a wide range of data processing functions including machine learning and graph analytics. It is scalable and dynamic, and can integrate with a range of tools and frameworks.

Apache Spark is excellent for handling very large datasets, performs processing in-memory, and supports real-time data processing. However, it has a complex setup and management, requires cluster resources, and could be considered overkill for scraping smaller data sets.

Apache Spark

10. Datamam

A great all-rounder class of tools, Datamam is designed to take an organization right from the beginning of a project to the end. The company develops a bespoke solution tailored to each organization’s specific data extraction needs, and is designed to meet unique requirements that may not be fully addressed by standard solutions.

Datamam’s custom tools can give organizations precise control over the functionality, features, and integration capabilities. Also, as it is built specifically for each client, the accuracy and reliability of the tools is high. Each project comes with extensive support. As a bespoke set of tools, Datamam does carry a cost and it is often more suitable for larger or more complex web scraping projects.

Datamam Codes

What’s next for your project?

There is a web scraping tool out there for every project, whatever the size, scale, and timeline. Whichever solution you go for, it is vital to explore it fully before making a decision, and be sure to test and make sure you fully understand how it works and how to navigate anti-scraping techniques.

The best way organizations that are starting from scratch with their web scraping knowledge can ensure they get the most out of their project for the least amount of time and resource spent on it, is by looking into a bespoke solution with a specialist provider. These specialists can design and develop a custom solution to make sure all your needs are met, and all the data that is extracted and parsed is accurate, reliable, and valuable, with the least amount of waste.

If you have further questions about web scraping, Get in touch via our contact page to find out more.


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<a href="" target="_self">Sandro Shubladze</a>

Sandro Shubladze

Building a World Inspired By Data

My professional focus is on leveraging data to enhance business operations and community services. I see data as more than numbers; it's a tool that, when used wisely, can lead to significant improvements in various sectors. My aim is to take complex data concepts and turn them into practical, understandable, and actionable insights. At Datamam, we're committed to demystifying data, showcasing its value in straightforward, non-technical terms. It's all about unlocking the potential of data to make decisions and drive progress.