Big data is rapidly growing in importance for businesses around the world. With so many companies generating large amounts of data, it is becoming increasingly important to be able to process that information and extract insights from it.
Java has historically been one of the most popular programming languages for big data. At the same time, Python has also become a viable option with its widespread adoption by major tech giants like Google and Facebook. The choice between Java or Python depends on your specific needs, but both are excellent options for anyone looking to work with Big Data in 2022.
Java and Python are two among various programming languages that have been used for big data. Java is one of the traditional languages, and Python is a newer option. Both languages have their advantages and disadvantages when it comes to big data. Here we will elaborate on the differences between both programming languages concerning big data.
What is Big Data?
The tools created to store, analyse, and manage this extensive data are known as big data. It is a macro-tool that has been developed to find patterns in the mayhem of this explosion in knowledge to create intelligent solutions.
Big data is a term that encompasses three key characteristics, namely volume, variety and velocity. Big datasets typically come from artificial intelligence (AI), mobile devices, or social media sources.
The programming languages that programmers widely use for big data are:
Java and Python are the two programming languages that dominate big data. Python stands out as a good choice because of its simplicity and ability to provide outstanding support in libraries that facilitate programming with Big Data. It also has fewer lines of code than Java hence reducing debugging time considerably compared to Java’s verbose coding style.
Java is the most popular and oldest programming language that incorporates many big data techniques. To get a clearer idea of which programming language is best suitable for big data, let us take a deeper look at its pros and cons.
Python is a highly readable, efficient and powerful high-level language with automatic memory management. It allows you to work as quickly as integrating systems while still having enough power for more complicated tasks like developing an entire application or framework from scratch.
Python is one of the most widely used languages in scientific research. It is because it has an easy to use and simple syntax that makes coding more accessible. It helps make quick prototypes more practical than other languages such as C or Java, which would take longer due to their greater complexity.
Some features that make Python a desirable programming language for big data are:
- Open source and easy to learn
- Simple coding
- Supports multiple libraries
- High processing speed
- Data processing support
- Scalability and portability
|ADVANTAGES OF PYTHON
|DISADVANTAGES OF PYTHON
|Versatility ExtensibleEasy to learnStabilityOpen source codeConvenient data structure
|Low-speed code executionWeak mobile and browser computingTyping restrictionsUnderdeveloped levels of database access
Apart from the advantages of python programming languages, Python possesses many advantages for working with big data. It includes:
- Versatility: Efficiency in loading, cleaning, submitting, and presenting data in the form of a website.
- Extensible: With the rich set of high-quality libraries like Numpy, Pandas, Bokeh, etc., it provides solutions to operate with large data sets.
- Easy to learn: Its intuitive syntax makes it relatively easier to learn
- Stability: Python is known for its stability and predictability in the developmental cycle
- Open source code: Its open-source code makes it easy to work with projects of any scale.
- Convenient data structure: The convenient data structure supports its object-oriented program paradigm.
Some disadvantages that you must keep in mind while working with big data include:
- Low-speed code execution: As each line of code runs line by line, the interpreted results are often slow in performance.
- Weak mobile and browser computing: Python is not that secure in this specific niche despite having a tremendous server-side.
- Typing restrictions: Python is dynamically typed. Hence, you do not need to declare the type of the variable when writing your code.
- Underdeveloped levels of database access: The database access layers are underdeveloped compared to JDBC (Java Database Connectivity) and ODBC (Open Database Connectivity). Hence, it is less commonly used in large companies.
Java is a versatile and popular programming language used in many of today’s data science techniques. It also forms the basis for Hadoop HDFS, an open-source framework designed to process big data applications.
It’s a C-like syntax with object-oriented elements that many developers well understand. Java is an excellent language for developers seeking to become more widely recognised in their field. The community-driven nature of Java means that it has support from many companies and organisations.
The striking features of Java with big data that can open up new opportunities and business models are:
- Automatic memory distribution
|ADVANTAGES OF JAVA
|DISADVANTAGES OF JAVA
|ReusabilityFast executionObject-oriented approachPlatform independenceFlexibilitySecurity
|VerbosityLow data science libraries
The major advantages of Java in big data include:
- Reusability: Java is famous for its reusability because of its reusable code.
- Fast execution: JVM (Java Virtual Machine) enables fast performance.
- Object-oriented approach: Java follows object-oriented programming concepts.
- Platform independence: Platform independence makes it easier to write code in one place and then execute it on any other site.
- Flexibility: A strong point is the ability to combine data science techniques with the existing code database.
- Security: Java takes care of code typing security, which is critical for large data projects.
Java has some disadvantages that you must keep in mind:
- Verbosity: Not very suitable for developing analytical applications that are complex and static.
- Low data science libraries: Java does not have many data science libraries compared to R.
To choose a programming language suitable for your big data, you must crucially analyse the pros and cons before making a choice. The table given below will help you make a logical decision between Python or Java for big data.
|The high-level language has short syntax and code readability features. It is easier to learn and use.
|A general-purpose language where you can write the code once and run it anywhere.
|It is not easily distributed and is slower compared to Java.
|It is easily distributed due to its popularity.
|Python is 5 to 10 times more productive than C++ or Java.
|Java is less productive than Python because of its need to define each variable.
|Complexity of Syntax
|It is not that complex, as it does not have the hardcore rules for braces and semicolons.
|It is a bit complex to understand, as it uses hardcore rules for braces and semicolons.
|Speed and Usage
|Python is slower compared to Java because of determining the time of the variable. It is easy to work with data science, machine learning, and web development.
|Fast execution of code compared to Python. Java is mainly used in Android and web development applications.
A language is more than just how it looks on paper. The rules of a programming language determine what programs you can build and the speed at which those instructions are executed. The debate between Java and Python has been a long-running one, but there is no perfect language. It solely depends upon your needs and demands. Big data projects require speed for execution to keep up with the order of our modern world while maintaining a straightforward approach everyone can use.
Viswanathan G is subject matter expert in Programming Language with extensive experience in training and project management. By academics, he is a Mechanical Engineer and also developed his skills in programming. His 25 years of teaching experience have brought in thousands of students across the world. He is an expert in designing training courses with technical content and real-time examples. He is now working as a trainer in Edoxi Training Institute.