Building a fintech product is no easy task – but the reward is also significant. Successful fintech companies such as Revolut, Stripe or Coinbase, reach multi-billion-dollar valuations and enjoy global customer recognition. However, in order for your business to become the next fintech unicorn, you first need to build a modern, reliable, and fully featured application.

Python is one of the hottest languages on the tech scene right now. Why? Well, it’s easy to learn, code in, and apply to different problems. No wonder that developers and businesses alike love it. At the same time, a technological breakthrough is changing the world of software and business – the ability to process huge amounts of data and glean useful insights from it. What do Python and big data have to do with each other? Well, the former happens to be the perfect tool to process the latter. How so? Read on to find out!

Organizations that want to survive and thrive on the current market need to ride the wave of digital transformation. Smart enterprises are now investing in software for boosting their financial operations, improving their employees’ productivity, and managing their resources with top efficiency in mind.

Python has been growing rapidly in comparison to other programming languages. There are a few reasons behind it – it’s fast, secure, battle-tested by top tech players and, more importantly, it’s getting more and more attention in the data science world. At Netguru, one of our core values is innovation. We want to work with the most effective solutions that match our clients’ needs. That’s why we couldn’t ignore the power of Python.

Python is one of the best and most popular programming languages on the technology market. Thanks to its power and versatility, it can be used for a range of applications – from simple scripts, to data analysis, to machine learning (ML). Which is why in 2018, we decided to include both Python and ML in our core tech stack at Netguru. During this intense year, we have grown our Python team to 11 experienced developers and built a number of applications using the language. Some examples include:

In order to create easily maintainable websites, a lot of companies decide to use content management systems (CMS). This way a lot of features like adding content, authorizing, and administration can be easily taken care of without a lot of programming knowledge. There are a lot of CMSs and they vary a lot, but most of them share the same idea of giving the user the ability to manage his website the easiest way possible. The most popular CMSs are created in PHP and one of them is Drupal, the third-most popular system. It is mostly used to create knowledge bases and business blogs. However, if we look at the recent popularity of programming languages, we can easily notice that Python is gaining more and more popularity and PHP is going the other way. In this case, we will take a look at three Python-based CMSs and compare them with Drupal and see what comes of it.

Python frameworks can be divided into a few areas as Python is a very diverse language and can be used in various fields. Each of these fields has its own frameworks, some of which are more popular than others. One of the most popular fields Python is applied in is web development, which we will focus on today.

In the world of app development, performance optimization is a never-ending, yet necessary, job. One that can be fairly rewarding - and not only in terms of developers’ personal satisfaction... First and foremost, better performance very often leads to higher conversion and user retention rates, which is directly connected with profit gains.

Common in applications that range from risk management to cryptocurrencies, Python has become one of the most popular programming languages among financial institutions and fintechs. Its simplicity and robust modeling capabilities make it an excellent tool for researchers, analysts, and traders.

With artificial intelligence becoming smarter and smarter, the interest in NLP (natural language processing) has also been growing rapidly. Utilizing and developing the technology that helps machines understand and process human language has become a hot topic in many industries, because it has the potential to finally fill the gap in human-computer interactions.