Answer:
Java, Scala, Python all of the above support concurrent programming is the answer.
Explanation:
Java- The oldest and most popular programming language ever. There are some Features/benefits that make Java popular among big data developers and tool builders:
- Java is a platform-independent language, so it can run on almost any system.
- Java is portable through what is called the Java Virtual Machine (JVM). JVM is the foundation of Hadoop ecosystem tools such as Map Reduce, Storm and Spark.
- These tools are written in Java and run on the JVM. Java supports various communities such as GitHub and Stack Overflow.
- Java is a scalable, backward compatible, stable, production-ready language. It also supports various well-established libraries.
The main drawback of Java is verbosity.
- Achieving simple functionality in Java requires writing long lines of code (number of lines of code).
- It does not support Read-Evaluate-Print-Loop.
- This is a big problem when choosing a programming language for big data processing.
Scala is a hybrid functional programming language with characteristics of both object-oriented and functional programming. As an OO programming language, it treats all values as objects and all OOPS concepts apply. As a functional programming language, Scala defines and supports functions. All operations are performed as functions. There are no stand-alone variables.
Scala and Java are popular programming languages that run on the JVM. The JVM makes these languages framework-friendly.
Scala features/benefits:
- It is a general-purpose object-oriented language with functional language features.
- It's portable because it runs in the JVM.
- Easily support Java APIs.
- As a Spark native, it is fast and robust in the Spark context.
- It's a statically typed language. Scala supports Read-Evaluate-Print-Loop (REPL)
Scala pros/cons:
- Scala is complex to learn because it is a functional language.
- steep learning curve.
- Lack of mature machine learning
Python- Python is one of the de facto languages for data science. It is a simple general-purpose open-source language and very easy to learn. It has a wide range of libraries, utilities, and out-of-the-box features, and supports many mature libraries for machine learning, big data processing, and visualization.
Advantages of Python:
- It is an interpreted language (that is, REPL, Read, Evaluate, Print, Loop support).
- When you type a command into the command line interpreter, it responds immediately.
- Java does not have this functionality. Easier to learn, easier to debug, and has fewer lines of code.
- Dynamically typed. That is, you can dynamically define the type of your variables. In other words, Python as a language is type-safe.
- Python is platform-independent and scalable.
Cons/Disadvantages:
- Python is slow. Big data experts find projects built in Java/Scala to be faster and more robust than those built in Python
- Python doesn't support heavy fork() in uWSGI, but it doesn't support true multithreading.
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