Proven data parsing tools. My experience and case announcements

Hello everyone! Sasha is here, and today I'll share with you some of the proven data parsing tools that I've been using for several years. And this is not just another review from a theorist - I have a lot of practical experience in this area and I have something to tell. To avoid being empty talk, I will publish a survey at the end where you can choose which case of the topic to publish first.

Banal Introduction

Throughout my career, I have tried countless tools and I can confidently say that there is no universal solution that would be suitable for all tasks. It is important to always rely on the goals and object of parsing. With an extensive range of solutions in your arsenal, you can always find the most productive, efficient, and economical. Now let's get down to specifics.

Specialized Software

Let's start with specialized software for data parsing. My favorite is Aparser. A powerful server parser can work both on a local machine and on a cheap VPS.

It solves almost any task, but the configuration may seem difficult. When I talk about complex configuration, I mean the interface. It will be difficult for a novice to understand, but if you figure it out, you can already stop =)

I use this #parser when I need to collect data and I have no other simpler ways to set it up. If I can't write a preset to collect the data I need on my own, I go to support. The guys prepare and provide a config based on my requirements for a fee.

This config is imported into the program in two clicks, and you can start collecting.

This software works stably, and one of its advantages is the ability to purchase high-quality proxy packages that provide phenomenal performance. By the way, the software is very fast and works with 1000 threads (oh my!).

Using #python

Recently I have been using this approach specifically to solve applied tasks. Especially when they are one-time and simple. First I study the queries in the developer console and export them to Postman. In Postman I get a ready-made code snippet that is easy to adapt for further development.

After tinkering in the development environment I get a multi-threaded bid parser for any list of queries with database writes.

This method allows me to parse various projects easily, and about 90% of my tasks are solved this way. Both simple libraries are used: http, requests, as well as specialized ones: scrapy, selenium, beautiful soup.

Cloud parsers

It is also worth mentioning cloud parsers, which do not require complex configuration. Here I want to mention #Apify and similar services. You can choose from a catalog of ready-made parsers or create your own parser for a specific project.

Working with Apify comes down to registration, choosing the desired site (for example, #amazon or #linkedin ), selecting a preset, entering links or a search query, and getting parsing results.

You immediately get access to server resources such as RAM, CPU time, proxies, and traffic. Some presets have a fixed fee, such as $40 per month. I consider it to be a very convenient service for fast data parsing.

I hope this article on proven data parsing tools was helpful for you. As I mentioned earlier, there is no universal solution for all parsing tasks, but with the range of solutions available, you can always find the most efficient one.

If you have any questions or need help with parsing, feel free to contact me. I have extensive experience in this area and will be happy to assist you.

Thank you for reading and don't forget to subscribe to my blog for updates!

P.S.

If you need help with parsing, write me - I am experienced and I can help you to find a solution of problem!

Contact me via private messages or Telegram channel https://t.me/+JmL3DDrzneBhOGEy