Different tech

Let's take a look on technologies that could enrich, renew your stack and how it can help. Consider, make a conclusion by yourself. It's not surprise that I, or any other back-end developer can work with PHP, SQL databases (MySQL, SQLite, etc), some non-SQL, JavaScript (libs like jQuery), Linux systems and system administration, and so on... I focus, frame and pick up for you just rarer, meantime, worth stack. I wanna introduce you what is in-demand, not so easily available (offered by all), open-source (e.g. if I overgeneralize what I'm not gonna do, I say that Java isn't generally considerable an open-source, free platform), and can interest you.


Code, not just click installs

You can think, deployment of programming - a creative complex problem-solving in an algorithmic manner offers more solutions than usually reused, (for you) overpriced, and prefab one-click installs:

See a 'development'What you find in cPanel


Haproxy, Actix, LiteSpeed

A better web software performance = less expenses for physical servers (hosting). See a case study or/and benchmarks from 3rd parties: Actix, Haproxy, and LiteSpeed vs Apache. Remeber Apache and see:

Web usage statsIs it a current topic?

circuit horse

Django, Flask, Tornado, etc

Frameworks that scale, enable a fast development and rich applications of general purpose programming, connectivity to edge cutting libraries, and GPU computing.  See Tornado and compare:

The most used CMSWith installed plugins



Can it interest you if you can save, get a work power (e.g. of 7 people worth £80K+/year), army that doesn't cost?

A Google prediction


GraphQL and Redis

Modern, nowadays database systems that provide graph and new abilities, higher speed, and connectivity.

See Redis


Analytics and ML

In math is a truth and if it's automated or not, statistics give a direct point of view that points to any improvements

Libraries (with PyPi)

This is what you can consider an example of successful stories 

Linux distros, aerospace companies and institutions such as NASA, or IoT (Internet of Things) and industrial machinery producers use Python for automation.  YouTube, Spotify, Google's AIs, crawlers, and web pages are vastly run by Python. Companies like Facebook, Twitter, and Google released a nutritious amount of free software that they use (also by themselves) for anything from basic web tasks to data science (AI, analytics/stats, data mining, etc... that you already find even in basic packages of Anaconda packed with JupyterLab), and automation.  I mean a lot of free but top, edge cutting software like e.g. these open source projects. Python is the main language of data scientific tools (perhaps, generally in science alongside R). Instagram used Django (Python), Haproxy, GraphQL, plus Redis; Twitter has deployed Haproxy, GraphQL, and uses also Python. The company behind GitHub (the biggest pages that host programming repositories) developed Haproxy, uses Python, and Redis. Websites of Pinterest, NASA, Mozilla, National Geographic, Disqus, Washington Post, The Onion, and many other companies (having an "in-house" employed tech guy), they use Django (Python). Reddit, PayPlug, Netfix, MIT University, Maligun, Cloudify, Zillow, Keen IO... use Flask (Python). Facebook uses Tornado powered by Python (Facebook doesn't limit itself only on PHP, but uses it too, slowly moving away) and with Yelp, Coursera, Google Cloud, Medium, Product Hunt, StackShare, Tumbir, and Artsy, all use GraphQL. Yahoo!, Airbnb, Uber, Stack Overflow (world's biggest community of programmers) use Redis and, partially, too Python.

Do I recommend others what I use by myself? Why?

Due to my studies in computer science (for a university degree) along with independent (e.g. this one Phytonic MOOC of multiple) MOOCs in data science, and own interests in automation, Python and networking have been a familiar way to go. Even, extensively, without any technical needs to utilize it all (but in an exploratory and learning/exercising manner that came before commercial and open source projects, this website itself and other back-end apps are run by multiple frameworks (e.g. Nickel) rather written in Rust and Python 3. Rust is new but the most favorized language between programmers which alternates C++ for higher speed and memory safety characteristics. To compare, see Nickel.rs (here, you find a full performance test if you scroll down), and Actix (check the benchmarks). Both of the frameworks are multiple times faster than PHP 7, faster than Go language frameworks and can handle more connections than Node.js. For example, I could mention that asynchronous Tornado web framework with Asyncio can be deemed as a Python alternative of Node and either Django, or Flask scale and more, are well suited with Multiprocess or Celery (apps written in PHP and many other languages can run only on a single CPU core/treat per an end-user). The main reasons for Python are scalability, multipurpose usability, available (free, open-source) libraries, and development speed/expenses (I worth my personal time and deadlines). In a planing manner for a future growth and adaptation to a changing environment (the environment will require a usage AI technologies, we'll minimally need to equal our tech stack with a competition), it doesn't really matter if you ask me, any random (good or bad) computer/data scientist, or well-priced employees of IBM, Google, Microsoft, or any other big company frequently working in automation, analytics, machine learning, advertisement, and other fields that are related or direct sub-fields of AI. Most often and reasonably, they consciously and helpfully advise you to stick with Python libraries and general connectivity to the language.