When Guido van Rossum developed Python in the 1980s, seldom did he know that it would become the world’s most widely used programming language. More than 8 million developers today, use Python as their primary development language. All thanks to its hassle-free code, an abundance of libraries and plethora of applications. Be it website development, machine learning or analytics, Python is being used everywhere and anywhere that people can think.
One of the most important implementations of Python is observed while analyzing data. Ever since the wave of digitization swept industries off their feet, the world has been blessed with plentiful data. Be it healthcare, business or any other industry. Developers are utilizing Python for data analytics, and it is proving to be of great importance.
There are more than just a few advantages of data analytics. On the one hand, it is helping people see the future with evidence from the past and the present. On the other hand, it is facilitating better decision making in all the processes. The point is, whatever people or industries want to accomplish with the data, Python is assisting in it. It is a tool that makes complex and tangled data appear sorted and clear. In other words, Python helps see data as information.
But as the world evolves, new programming languages keep on emerging. They are born out of the shortcomings or deficiencies of existing languages and help improve the performance of the system in one way or other. One such programming language is Google’s Go.
Golang or Go is a language developed by Google, whose idea was realized in the year 2007. However, it was only in 2009 that the world saw its first release. It is relatively much fresh than Python, considering the timeline both were born. In spite of being known for ten years, why are people realizing Golang’s importance now more than ever? Is it because the light around Python is dimming? Whatever be the reason, recent events have suggested that Google’s Go has more than a few advantages over Python, especially when we talk of data analytics.
Google’s Go was born out of the need for a language that was loosely based on the syntax of C, while eliminated the extraneous garbage of C++. As a result of this, the lead developers at Google Robert Greisemer, Rob Pike and Ken Thompson created Go with many features of modern languages. Having said this, developers can easily find object-oriented features such as operator overloading, pointer arithmetic and type inheritance along with others. Apart from this, Go also has a robust library with unmatched performance and speed.
Even though Python can do a lot of what Go does, it lacks in some of the critical aspects. When it comes to speed, dynamic typing, GIL, concurrency support etc. Python shows clear signs of limitations for analytics. Let’s take a more in-depth look at what this comparison between Go and Python mean for analytics-
With large amounts of data on the table, it becomes fundamentally necessary to have an optimal performance during analysis. Think otherwise. The analysis would be a prolonged process when data are abundant, and the developed system takes massive amounts of time to derive meaning out of it. However, we are somehow able to do it with Python. It might not seem as slow, but when compared to Google’s Go, it is easy to understand the difference.
Be it memory usage or time spent in a mathematical calculation, Golang is faster and less complicated than Python. However, anything you want you to build in Go can be called in Python.
Python has also been in the news recently because Golang replaced it. The cloud-based software company Salesforce thought it was better to use Google’s Go instead of Python for their data analytics program, Einstein Analytics. While this may come as a shock to Python lovers, the reality is that it’s challenging to write long lines of code in Python. Moreover, today’s applications demand scalability because of rapidly changing user needs.
If something is limiting the performance or growth of the system, it just turns out to be detrimental for a business. In the case of Salesforce, the company observed that Python miserably failed at multi-threading. And considering the future scope of scaling Einstein Analytics, continuing to use Python was a wrong choice.
There is nothing that can deny the fact that every language is built with a purpose of its own. Having said this, programming languages find their applications in related niches and continue to extend their reach. The abundance of libraries in Python gives it an upper hand for data analytics. Be it Pandas for data manipulation and analysis, Matplotlib for plotting and visualization, StatsModels for statistical modelling, testing and analysis or Seaborn for statistical data visualization. There is no end to the libraries you can use for data analytics in Python development.
On the other hand, Google’s Go is more focused on being a system language that finds its acceptance in the field of cloud computing and cluster computing. In data analytics, its usage can be extended because of either its performance or that fact that it offers concurrency.
In another instance, Python does not offer any memory management, while Go does.
Python is one of the best languages for basic programming. However, if you have to write large amounts of code, it is best to use Go. Python’s lack of memory management, dynamic typing facility as well as the complexity of code with increasing length, make it an out-of-the place option for extensive system development. Golang, on the other hand, is inclusive of a lot of faster and more transparent in-built functions but lacks data analytics libraries that Python supports. In other words, when the field of data analytics demands concise and quick answers, nothing can beat Python. However, if one is looking for a more business-oriented solution, Go is a preferable option.