We're moving from Java to Kotlin with our Microservice Stack (Spring Boot) because it is excellently supported by framework and tools and the learning curve is not very steep Kotlin is way more straightforward and convenient to use while providing less boilerplate and more strictness, which finally leads to better code, which is more readable, maintainable and less error-prone. We especially like Kotlin's (functional) data structures, which are, e.g. compared to Scala, easier to understand and don't require deep knowledge in functional programming.

Expo was a tool Macombey really wanted to utilize from the beginning. I have been working with React Native since 2016 and originally I had to use simulators in Xcode, install pods on top of node packages, configure certificates, and more abundant objectives that take time away from actual development. As a development studio, we have to move quick and get projects to our clients and partners in a matter of months.

Expo made this easy for us. We now have a mobile app for clients to download and test their project on, there is no need to install pods or configure Xcode, and development is super fast and reliable now.

I am working in the domain of big data and machine learning. I am helping companies with bringing their machine learning models to the production. In many projects there is a tendency to port Python, PySpark code to Scala and Scala Spark.

This yields to longer time to market and a lot of mistakes due to necessity to understand and re-write the code. Also many libraries/apis that data scientists/machine learning practitioners use are not available in jvm ecosystem.

Simply, refactoring (if necessary) and organising the code of the data scientists by following best practices of software development is less error prone and faster comparing to re-write in Scala.

Pipeline orchestration tools such as Luigi/Airflow is python native and fits well to this picture.

I have heard some arguments against Python such as, it is slow, or it is hard to maintain due to its dynamically typed language. However cost/benefit of time consumed porting python code to java/scala alone would be enough as a counter-argument. ML pipelines rarerly contains a lot of code (if that is not the case, such as complex domain and significant amount of code, then scala would be a better fit).

In terms of performance, I did not see any issues with Python. It is not the fastest runtime around but ML applications are rarely time-critical (majority of them is batch based).

I still prefer Scala for developing APIs and for applications where the domain contains complex logic.

1 code deploys for both: Android and iOS. There is a huge community behind React Native. And one of the best things is Expo. Expo uses React Native to make everything even more and more simple. Awesome technologies. Some other important thing is that while using React Native, you are reusing all JavaScript knowledge you have in your team. You can move easily a frontend dev to develop mobile applications.

A huge PRO of Expo, is that it includes a full building process. You run 1 line in the terminal, and 10 minutes after you have 2 builds done. Double check EAS Expo.

We needed to incorporate Big Data Framework for data stream analysis, specifically Apache Spark / Apache Storm. The three options of languages were most suitable for the job - Python, Java, Scala.

The winner was Python for the top of the class, high-performance data analysis libraries (NumPy, Pandas) written in C, quick learning curve, quick prototyping allowance, and a great connection with other future tools for machine learning as Tensorflow.

The whole code was shorter & more readable which made it easier to develop and maintain.