Python Vs Java for Big Data: Choose The Right Programming Language?

Python Vs Java for Big Data: Choose The Right Programming Language?

When it comes to data science, the most common languages are Python and Java. According to a recent survey, Python has been billed as the most popular programming language, beating C++ and Java. The incredible growth of Python has labeled it the most preferred language than Java for developing machine learning applications. Where Java is an object-oriented language in its purest form, Python is more of a scripting language. Since both Python and Java are specialized tools, they both are versatile and efficient. They can be used for a wide variety of development projects, mobile apps, APIs, IoT, data science, and more. So, which programming language is suitable for your big data application development services? Let us find out by weighing Python Vs Java for big data.

Python Vs Java for Big Data

Before you choose Java or Python from a trusted Java development company, it is wise to do your homework first. So, let’s first see the general difference between both these programming languages.  

Compilation program: Java easily compiles on any platform. Whereas, Python can only compile with Linux.

Type Of Language: It is a general-purpose language where the developer writes the code and runs it everywhere. Whereas, Python is a high-level language. It delivers short syntax and code readability features.

Code Length: Java code is lengthy because each program has to be written in a class. Whereas, Python codes are small and developers can directly write the code.

Distribution: Java software is popular. Therefore, it can easily get distributed. Whereas, Python is slower than Java, which makes it not so feasible for distribution.

Productivity: Java is comparatively less productive than Python because it needs to define each of the variables. On the other hand, Python has fewer lines which makes it 5-10 times more productive than C++ and Java.

Ease of Typing: Java restricts the developers to define the exact type of variables. Whereas, Python doesn’t restrict us to define the type of variables. Thus, typing becomes easy in Python.

Type: Java is statically typed, which means all the variables must be explicitly declared. Whereas, Java is dynamically typed. So, we need not declare anything.

Syntax Complexity: The Java syntax is complex to understand, as it uses hardcore rules for braces and semi-colons. Whereas, Python is not so complicated because it eliminates the usage of braces and semi-colons.

Popularity: Developers have been using Java for a decade. It is the most-trusted programming language for Android and web development applications. Whereas, Python is best-suited for Data science and machine language.

Speed: Java is quick at code execution. Whereas, Python is slower because of determining the type of the variable at run time.

Advantages and Disadvantages of Python in Big Data

Python is an ideal choice for web development services because of the following reasons:

Versatility: Python language offers an efficient ecosystem for loading, submitting, cleaning, and displaying data in the form of a website.

Perfection: Its rich ecosystem of high-quality libraries such as NumPy, Pandas, Matplotlib, Bokeh, and more are keen on providing out-of-box solutions.

Easy to learn: Its intuitive syntax and high-activity environment make it one of the most easy-to-learn programming languages.

Stability: It is highly stable and predictable in the context of the development cycle.

Programmers and developers consider Python as an ideal choice for working with big data. Thanks to its open-source feature with thousands of libraries, making it is convenient for all types of projects. The Python and big data ecosystem make it easy and fast to analyze data and prototype machine learning solutions. Some other advantages of Python are:

• Large dedicated community
• Open source code
• Extensive library
• Accessible support system
• Easy to understand specifics
• Easy data structure
• Supports object-oriented programming paradigm

Disadvantages of Python for big data

Like any other programming language, Python also requires some improvement and up-gradation. The major areas where it lacks are:

Speed: The Python codes run line by line, which results in slower execution. However, slow speed may not bother you because big data doesn’t require high speed.

Less mobile and browser computing: While Python serves as an ideal server-side language it is rarely used for mobile applications due to its less secure environment.

Restricted typing: As mentioned earlier, Python is dynamically-typed. It means you need not declare the type of variable while writing the code. On the other hand, Duck typing may cause runtime errors.

Underdeveloped stages of database access: When compared to popular technologies such as JDBC and ODBC, Python database access layers are underdeveloped. Thus, it is less commonly used in large enterprises.

Advantages and Disadvantages of Java for Big Data

Some of the major advantages of Java include:

• Reusable codes
• High speed
• Object-oriented approach
• Independent platform
• Flexibility to integrate data science methods
• Highly secure

Java and big data go along as it is highly efficient and widely used for high-performance coding (ETL) and machine learning algorithms.  

Disadvantages of Java for big data

Java’s verbosity may not be very suitable for developing complex static and analytical applications. It also doesn’t have many Java Data Science libraries to perform static methods

Java Vs Python – Which one is right for me?

If speed is your goal, Java is an ideal choice for your big data, as it handles simultaneous execution of multiple codes and is more suitable for cross-platform applications.

However, before starting your project involving big data, one must list down the requirements and expectations upon which they should choose Java or Python. For more queries on Java programming development, hire Java developers from a professional organization like ours and we shall make sure to deliver expected outcomes.

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FAQs

How is Java a platform-independent language?

In Java, the JVM depends on the operating system of your choice. For instance, if you run MacOS X, you may have a different JVM whereas, for Windows and some other operating systems there will be a different JVM. Therefore, while downloading it, you may be given a list of JVMs and you will definitely choose the JVM targeted for the operating system that you are running. Thus, Java is a platform-independent language.

What is Aggregation in Java?

When object A contains a reference to another object B, it is defined as Aggregation. Aggregation helps in reusing the code.  

How can you copy objects in Python?

In Python, we use the “= “operator to create a copy of an object. In Python, you may think it creates a new object, but it doesn’t. It only creates a new variable that shares the reference of the original object.