Different programming languages have unique structures and formats, so that their use is driven more with preferences, tendencies of IT cultivation, and business objectives. When it comes to data science, the most common choice language is Python and Java. Are there fundamental differences between them, because both have certain similarities, and does it make it difficult to choose a tool for a project?
This is a high-level programming language java technical interview questions based on object-oriented paradigms. Java is the language oriented language in the purest form, while Python is more than a scripting language.
As a special tool, both are versatile, efficient, and can be used for various development projects, from cellular applications and fire to IoT, data science, and other solutions. So, what should be based on the choice? Let's put Java and Python under a microscope and start with the general concept.
Big data programming language
Programming language is a tool used to instruct computers to carry out certain actions. Among the most prominent large data tools are:
R.
Scala.
Java
Python.
R is open source language, but it is better to use for statistics, visualization, and data modeling rather than analysis. The general purpose tool is quite strong, but cannot be used as the general purpose language. Although, the language is advanced, has many possibilities and quickly gained popularity. However, for example, community support and the number of libraries are greater available for Python.
Scala is an open-source programming language, a high level which is part of the JVM virtual machine ecosystem. Popular in the financial sector, this code is efficient but easily bloated and the application can be slower than written on Java. Scala is not ideal for parsing large data because it does not have syntax and library.
The program is usually encoded in an integrated editor or development environment (IDE) with language rules, syntactics, and structures in mind. So this solution is more for large-scale analytic tasks. However, Apache Spark cluster computing infrastructure for large data applications was written entirely in Scala.
While abundant options, java and Python dominated. Java is the most popular, with around 9 million programmers using it. In second place is Python, which is preferred by 5 million programmers. Both can be used to develop full stack applications, support server-side models, clients and databases. Let's get to know them better.
Python for large data
When it comes to large data, Python is a high-level language that is very easy to read, efficient, and strong with automatic memory management. This is the older than two languages. NASA uses it to program space equipment.
This allows you to integrate the system quickly and efficiently. Dynamic Python, supports several programming paradigms, including OOP, functional and procedural programming. Language objectives are simplicity, beauty, clarity, reusability, and readability of the code. It's a good scale and can be used to build a variety of systems.
The more entry-level programmers are considering Python as the top language, and their popularity grows. Simply simple and easy to learn but missing in getting updates. Python is supported by a large data framework, but at the same time, the new spark feature is more likely to come out first for scala / java, while pyspark may require some new new versions.
Python has gained a lot of popularity in recent years thanks to the development of artificial intelligence, machine learning, and data science. It is best compatible with machine learning and data analysis, any activity that includes static graphics, mathematics, automation, multimedia, database, text processing and images.
The main benefit of Python is a large library that is able to carry out multi-level tasks. When evaluating Java vs Python's abilities for large data, it is best to compare the advantages and disadvantages of each.
Java benefits for large data techniques
The main advantage of Java for large data includes:
Reusable code;
Speed - JVM is used for timely complications;
Object-oriented approach;
Independence platform - one time recording, launched in any place with Java Virtual Machine;
Flexibility - The ability to integrate data science methods with the existing code database is a great added value;
Security - Java takes care of security code security, which is important to develop large data solutions.
Java is a very efficient compile language that is widely used for high-performance coding (ETL) and machine learning algorithms. That's why big data and Java are great friends.
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