Need of Python in Big Data

1. Open Source:

Python is an open-source programming language developed beneath under an OSI-approved open supply license, creating it freely usable and distributable, even for business use.

Python is a general-purpose, high-level interpreted language. It doesn’t have to be compiled to run. A program known as an interpreter runs Python code on virtually any type of system. This implies that a developer can modify the code and quickly see the results.

2. Easy to learn:

Python is very easy to learn just like the English language. Its syntax and code are easy and readable for beginners also. Python has a lot of applications like the development of web applications, data science, machine learning, and, so on.

Python allows us to write programs with lesser lines of code than most of the other programming languages. The popularity of Python is growing rapidly because of its simplicity.

3. Data processing Libraries:

When it comes to Data Processing, Python has a
rich set of tools with a whole range of benefits. As it’s an open-source language, it is easy to learn and also continuously improving. Python consists of a list of various useful libraries for data processing and also integrated with other languages (like Java) as well as existing structures. Python is richer in libraries that enhance its functionality even more.

4. Compatibility with Hadoop and Spark:

Hadoop framework is written in Java language; however, Hadoop programs can be coded in Python or C++ language. We can write programs like MapReduce in Python language, while not the requirement for translating the code into Java jar files.

Spark provides a Python API called PySpark released by the Apache Spark community to support Python with Spark. Using PySpark, one will simply integrate and work with RDDs within the Python programming language too.

Spark comes with an interactive python shell called PySpark shell. This PySpark shell is responsible for the link between the python API and the spark core and initializing the spark context. PySpark can also be launched directly from the command line by giving some instructions for interactive use.

5. Speed and Efficiency:

Python is a powerful and efficient high-level programming language. Whether for developing an application or working to solve any business problem through data science, Python has you covered all these boundaries. Python always does well for optimizing developer’s productivity and efficiency.

We can quickly create a program that can solve a business problem and fills a practical need. However,
the solutions may not reach optimized python performance while developing quickly.

6. Scalable and Flexible:

Python is that the most well-liked language for ML/AI due to its convenience. Python’s flexibility additionally permits to instrument Python code to form ML/AI scalability possibly without requiring higher expertise of distributed system and lots of invasive code changes. Hence, ML/AI users get the advantages of cluster-wide scalability with minimal effort.