## 5 Conclusions  
Considering the needs and challenges faced by engineers and programmers in the context of a more comprehensive realization of the concept of Industry 4.0, various methods and means should be sought to make this possible. Of course, the problem indicated is extensive and cannot be addressed in one publication. Therefore, the authors focused only on using selected programming languages to implement the decision tree algorithm in the context of their use in decision-making processes dedicated to solutions that enable customers to analyze the selection of elements in a remote rapid prototyping service. This service's choice as a source of data was not accidental. Considering the current epidemic situation and related limitations and difficulties in transport, communication, and cooperation seemed essential and exciting to the authors. However, nothing stands in the way of other data from the area of economics, CNC production systems, or sensor and control systems. Given that the vast majority of hardware solutions used in industrial environments, particularly Industry 4.0, are usually embedded systems, the intervention in their architectural design is restricted, including in the area of memory expansion and processor replacement. Thus, optimization of the code, e.g., its size, required memory, and processor load, is an important issue. Obviously, specialized server systems, especially cloud computing, offer great possibilities of scalability of necessary resources. Still, such solutions in the area of industry can be used only to a narrower scale. Analyzing the experiments' results, it can be concluded that the Golang programming language is an alternative to the more widely used Java and Python languages so far. It has excellent implementation potential and is characterized by a fresh approach to programming. At the same time, it is easy to learn. In terms of calculation time and resource management, it does not differ significantly from Java and Python applications, and in many cases, it performs better. From the point of view of resource usage, both languages, Go and Python is very similar. Nevertheless, it is important from a programming and performance point of view to emphasize the following aspects of Go compared to other languages: syntax simplicity, C-based syntax, cross-platform and cross processor, as well as native compiles with no runtime installation required. Go can be an attractive alternative in the area of DevOps tools. It is attractive to build something small-medium that works natively without using a lot of RAM and which runs fast with many things needed for this task in the language itself. However, in Go's case, the most significant innovation is probably the efficient concurrency implemented with channels and goroutines: synchronous yet non-blocking IO use goroutines, which are essentially functions that can work simultaneously and independently. Goroutines are very scalable because they take up little memory and run many processes simultaneously, while Java threads are inherently blocked. Golang goroutines are the opposite of what a Java thread is, which turns out to be a very memory consuming technique. This feature of Java became apparent during the research, as this language had the worst results from the point of view of the memory used. On the other hand, Python uses concurrency, but it is not built-in; it introduces parallelism utilizing threads. This means that if you are going to work with large datasets, Golang seems to be a more appropriate choice and much more scalable. Modern software must be high-speed, and delays of milliseconds are of great importance, especially in the case of large data sets. However, in embedded and dedicated systems under IoT and Industry 4.0, it is certainly more critical to optimize a given program's operation and effectively manage resources. One of Golang's characteristics in terms of its efficiency is channels.