Python or Java — Choosing the best programming language for Data Analytics

# Big data programming languages :
A programming language is a tool used to instruct a computer to perform a specific action. Among the most notable big data tools are:

1. Python
2. Java

# Python for big data
When it comes to big data, Python is a highly readable, efficient, and powerful high-level language with automatic memory management. It is the older of the two languages. NASA uses it to program space equipment.
It allows you to work quickly and efficiently integrate systems. Python is dynamic, supporting several programming paradigms, including OOP, functional and procedural programming. The goals of a language are simplicity, beauty, clarity, reusability, and code readability. It scales well and can be used to build a wide variety of systems.
More and more entry-level programmers are considering Python as the top language, and its popularity is growing. It’s pretty simple and easy to learn but lags behind in getting updates. Python is supported by the big data framework, but at the same time, new Spark features are more likely to come out first for Scala/Java, while PySpark may require several new minor 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, math, automation, multimedia, databases, text and image processing.
The main benefit of Python is huge libraries capable of performing multi-level tasks. When evaluating the capabilities of Java vs Python for big data, it’s best to compare the advantages and disadvantages of each.

pros and cons of python for big data
Advantages of Python in big data
Python is a very good choice for working with big data because it is:
Versatile: The language is efficient for loading, submitting, cleaning, and presenting data in the form of a website (e.g., using the libraries Bokeh and Django as a framework).
Perfect for extensibility thanks to a rich ecosystem of high-quality libraries Numpy, Pandas, Matplotlib, Bokeh, Tensorflow, Scikit-learn, and Nltk, providing out-of-the-box solutions, for instance, for working with large datasets or visualizations.
Relatively easy to learn thanks to the intuitive syntax and high activity of the environment.
Stable and predictable in the context of the development cycle.
Python has overtaken R in analytics in recent years. Programmers consider it to be the best for working with big data. Open-source, with thousands of libraries, it makes it easy to work with projects of any scale. For example, Numpy allows you to achieve C-like speed when working with vector and matrix math, while Pandas can vectorize operations that easily cleanse and transform huge amounts of data.
The Python and big data ecosystem makes it easy and fast to analyze data and prototype machine learning solutions.

# We can say that the main advantages of Python are as follows:
Huge dedicated community;
Open source code;
Extensive library;
Accessible support;
Easy-to-grasp specifics;
Convenient data structure;
Support of the object-oriented programming paradigm.

# Disadvantages
Python is a great choice, but you should also be aware of the possible consequences:
Lower speeds. Python code runs line by line, and because it is interpreted, it often results in slow execution. This is not an issue if the project doesn’t require high speed, as Python has many other advantages.
Weak mobile and browser computing. While Python serves as a great server-side language, it is rarely used to implement mobile applications. The reason is that it is not that secure in this specific niche.
Restrictions on typing. Python is dynamically-typed. This means that you don’t need to declare the type of the variable when writing your code. However, Duck typing can cause runtime errors.
Underdeveloped levels of database access. Compared to more widely used technologies such as JDBC and ODBC, Python database access layers are underdeveloped, so it is less commonly used in large enterprises.
This is irrelevant to our topic, but skeptics argue that too much ease of writing code reduces the motivation to learn other languages, such as more verbose Java. Despite some speed and security issues, Python is a great big data language.

### Java for big data
Java is one of the first programming languages widely renowned for its versatility and incorporating many data science techniques. It is important to consider that the Hadoop HDFS platform for processing and storing big data applications is written entirely in Java. It is an object-oriented language with a C-like syntax that is familiar to many programmers.
It has a wide variety of uses and can work on almost any system. In big data, Java is widely used in ETL applications such as Apache Camel, Apatar, and Apache Kafka, which are used to extract, transform, and load in big data environments. Java and big data have a lot in common. In fact, they are synonyms as MapReduce, HDFS, Storm, Kafka, Spark, Apache Beam, and Scala are all part of the JVM ecosystem.
Investing in Java is beneficial for developers in the long run. The language has gained widespread community support (Stack Overflow and GitHub), and while not as optimized as Scala and not as powerful for data manipulation as R, it is still far better than either.
The code can be written once and then the program can be run on different platforms. Moreover, the compiled Java code can work for anyone. This language can be used to develop a wide variety of applications. And it is not for nothing that many consider it the fundamental programming language of big data because all major technologies are written in it.

# Advantages of Java for big data engineering
The main advantages of Java for big data include the following:
Reusable code;
Speed — JVM is used for timely complication;
Object-oriented approach;
Platform independence — one-time recording, launch in any place with the Java virtual machine;
Flexibility — an ability to integrate data science methods with the existing code database is a big plus;
Security — Java takes care of code typing security, which is important for developing big data solutions.
Java is a highly-efficient compiled language that is widely used for high-performance coding (ETL) and machine learning algorithms. That’s why big data and Java are great friends.

# Disadvantages of Java in big data
Java’s verbosity is not very suitable for developing complex static and analytical applications. It doesn’t have many Java Data Science libraries for static methods compared to, for example, R. But otherwise, it is a very suitable language for data science. It is not for nothing that many companies appreciate the ability to integrate readymade big data with Java codes into an existing database.

# Top 4 things to start a big data project using Python or Java
When starting a project involving big data, the Java vs Python should be based on what best suits your needs, taking into account the basic requirements.

Specificity
Be prepared to learn a lot. You will need to master various software packages and modules. With Python, this will take less time and effort. It’s better for a team that includes both developers and data scientists.

Flexibility
It requires the ability to not only write but also quickly extend applications with new features. Python is more flexible but less efficient than Java in big data.

Effectiveness
In the ever-changing science environment, there are many opportunities to get the desired result. From the perspective of heavy workloads and speed of network communication, a dynamic language may be less productive than a statically-typed language. But for medium-load applications and at the MVP stage, Python is more convenient than Java, due to the shorter development time for new functions.

Productivity
When working with big data sources, it is important to optimize the performance of your code. Strong typing provides less code, and concise syntax allows you to write readable code. But compiled technologies are faster than interpreted ones, so a reasonable balance of functionality is needed when choosing a language.