by Allison Zhang, Data Engineer, Virtulytix
In my prior blog, we discussed the differences between Python and R for data scientists. But before data scientists can analyze the data, there is one more important process, which is data ETL (Extract, Transform and Load).
As always, the answer is it depends.
From a novice’s view, Python is easier than Java. If this is your first time dive into the world of data or programming, Python can give you a quick introduction to key ETL concepts. Python is more readable than any other language. Its simple syntax is straightforward for everyone from experts to novice programmers. All you need to focus on is how to make the program produce your desired output. . This simplicity has made Python an extremely popular language within the business world and academia. A recent survey from the Association for Computing Machinery (ACM) found that Python has surpassed Java as the most popular language to introduce students to programming.
Once you understand basic programming concepts, it is time for you to move on to other more complex languages. This does not mean that Python is not an advanced programing language but, on the contrary, Python can achieve complex projects also. But more factors need to be considered at this stage.
Python is flexible. It is dynamically typed, which means Python performs type checking at runtime. This means that you do not need to declare the variable type when creating a variable . On the other hand, Java is statically typed, it has to declare the variables before the value can be assigned. So, Python is more flexible and can save time and space when running the scripts. But it might cause issues at runtime and it is slower than Java because of the type checking process. Java is strict. Strict typing makes it easier to provide autocompletion for Java. The compiler cab prevents you from mixing different kinds of data together. This is very helpful in the data engineering field. When a program consists of hundreds of files, it is too easy to get confused and make mistakes, and the more checks we have on our programs, the better off we are.
When it comes to performance, Java is a better choice since it is more efficient. Java is more efficient when it comes to performance speed thanks to its optimizations and virtual machine execution. Just because of Python’s flexibility, performance is slowed down, which makes Java more attractive in this perspective. Java’s history in enterprise and its slightly more verbose coding style mean that Java legacy systems are typically larger and more numerous than Python’s. The “write once, run anywhere” design philosophy adopted by Java makes it unique in nature. In addition, it is extremely scalable making it the numero-uno choice for enterprise level development. And for large scale data, Java is always a better choice. It is faster and more efficient. For instance, Apache are written in Java.
After all, which language to choose depends on the scalability, performance, and purpose you want to achieve. For very large datasets, Java performs better than Python because of the factors discussed above. However, when performance is not that important, both languages are suitable for data engineers.