Detailed comparison of parsing automation tools: how to choose the right parser for your task
Information is expensive, and the ability to collect and analyze data provides an advantage in business, marketing and research. However, choosing automation tools is not an easy task. This is the key point that determines how quickly, efficiently and painlessly you can complete the task.
To understand which tool is suitable, it is important to understand the details. This is not just a choice between “paid or free”, but about what tasks are set for parsing: from collecting competitors’ prices to complex cases bypassing anti-bot systems.
The whole point of any parsing: you pull it from the site and carefully fill out your table with the necessary data.
What is important to know before choosing a parser
Before choosing a scraping tool, there are three questions to ask:
- What data should be collected?
If the goal is to collect basic information like product descriptions, a simple tool will suffice. But if the task is to process thousands of pages in a short time, more complex ones will be required.
Price collection is almost the main reason why parsing is used in marketing.
- What restrictions will you have to face?
Most modern websites are protected from automated data collection. These could be captchas, restrictions on the number of requests from one IP, or even systems for tracking user behavior. The more protection, the more difficult the task.
Did everyone find out?
- What resources are available?
You have a choice: buy a ready-made solution or develop your own. Free tools require more time to understand, while paid ones will quickly pay for themselves due to the speed of work and minimization of risks.
What does this look like in practice?
Imagine you want to collect information about airline ticket prices. You have two options:
- Use a script that opens each page and manually retrieves the data. This will take days, and if requests from one IP are limited, the script will quickly be blocked.
- Connect a service with ready-made IP address rotation and built-in captcha processing. Such a tool will collect data in a few hours that would take a week to collect manually.
We are looking for where the price of air tickets is stored in the site code
Categories of tools for automating parsing
Parsing tools fall into three main categories: ready-made services, programming libraries, and visual parsing builders. Each of them has its own characteristics that need to be taken into account when choosing.
Ready-made services
These are cloud platforms that offer scraping out of the box. They typically provide an intuitive interface, minimal customization, and operate via a browser.
Example: ScraperAPI, Apify.
ScraperAPI promises to make scraping simple. And in general he doesn’t lie.
Pros:
- No programming required: just specify the URL and parameters.
- Built-in functions for bypassing captchas and rotating IP addresses.
- Automatic data processing: upload to CSV, JSON or database integration.
Cons:
- Paid tariffs. For example, scraping 10,000 pages can cost between $50 and $200.
- Dependency on the service infrastructure: if it is blocked on the target site, work becomes impossible.
Ideal for:
- Small and medium businesses. For example, for an online store that wants to monitor competitors’ prices.
What it looks like:
Let's say you need to collect information about the prices of smartphones on the marketplace. The user selects a ready-made service, configures parameters (product names, price range) and receives a ready-made table with the results.
ScraperAPI interface
Software libraries
These are tools for developers that allow you to build scripts for specific tasks. Most popular libraries:
- BeautifulSoup (Python): for HTML processing and data extraction.
- Selenium: to simulate user actions in the browser.
- Puppeteer (JavaScript): to control the browser and collect data from dynamic sites.
Sample parser code using the BeautifulSoup library
Pros:
- Full flexibility: you can adapt the script to any site.
- Accessing complex data: interacting with JavaScript, sending API requests.
- Free: the libraries themselves are distributed free of charge.
Cons:
- Requires programming skills. For example, to write a script in Python, you need to know the basics of the language and understand HTTP requests.
- More time to set up: Creating a script from scratch can take hours or even days.
Ideal for:
- Technical specialists and developers. For example, if a company wants to create its own tool for regular data monitoring.
What it looks like:
You are writing a script to collect data from a website. For example, you need to extract the names and prices of products. The code using BeautifulSoup processes the HTML and the requests library sends requests to the server. As a result, you get a list of data ready for analysis.
The script collects data in 5 minutes that would take you half a day to collect manually
Visual parsing constructors
These are drag-and-drop tools. They allow you to collect data without programming, with minimal technical intervention.
Example: Octoparse, ParseHub.
Octoparse Home Page
Pros:
- No programming skills required: just configure mouse actions.
- Supports working with dynamic sites and complex structures.
- The ability to visually see the parsing process.
Cons:
- Limited capabilities: Complex tasks may still require coding.
- Paid plans: Free versions have a data limit.
Ideal for:
- Newcomers. For example, for a marketer who needs to quickly collect contact information from a website.
What it looks like:
You launch the designer, select the necessary elements on the site (names, prices) and start collecting data. The results appear in the form of a table, ready for downloading.
How to choose a tool for your tasks
The choice of tool depends on the task, the amount of data and the level of site protection. Let's consider several scenarios.
Basic parsing for small tasks
Example tasks: Collect bus schedules from the transport company website.
Site characteristics: Simple structure, static HTML without parsing protection.
Recommended Tool:
- BeautifulSoup library (Python).
It allows you to quickly extract text from HTML pages. Its ease of use makes it an ideal choice for beginners.
Why:
HTML pages with schedules often do not require bypass protection. A 20 line script will collect all the information.
Collection of data from restricted sites
Example tasks: Collect competitors' prices on the marketplace.
Site characteristics: Limit of requests from one IP, simple captcha.
Recommended Tool:
- Selenium to simulate user actions.
- Proxy servers for IP rotation.
Why:
Selenium allows you to emulate user actions by bypassing simple protections. With proxy servers you can bypass the IP limit.
Advanced parsing with security bypass
Example tasks: Collecting data from a site with an anti-bot system (for example, dynamic sites protected by Cloudflare).
Site characteristics: JavaScript is used to load content, complex captchas, anti-bot protection.
Recommended Tool:
- Puppeteer (JavaScript) to handle JavaScript on pages.
- Mobile proxies to bypass anti-bot protections.
A piece of parser code in Python using Puppeteer. Required for parsing dynamic pages.
Why:
Puppeteer allows you to work with sites that load content dynamically. Mobile proxies will disguise requests as actions of real users.
Large-scale parsing with large amounts of data
Example tasks: Collect millions of records from several dozen sites.
Site characteristics: High level of protection, regular updates.
Recommended Tool:
- Ready-made services with API support, such as Bright Data or ScraperAPI.
Why:
The services provide a ready-made infrastructure with a proxy, captcha bypass and support for high load.
Steps:
- Configure the API by specifying parsing parameters (URL, keywords).
- Download the result to your system.
- Profit.
So we talked in detail about all the possible parsing tools and when to choose them. Don't forget about the proxies that we are always ready to provide you with. Parse wisely, parse with pleasure and benefit.