PySpark Vs Python: A Cognitive Analysis

In this era where everything is being compared to each other. We bring to you yet another Comparison of PySpark Vs Python. PySpark is a Python-based API that uses the Spark framework in combination with Python. But, we all know that Spark is the Big data engine while Python is a programming language. So what is really the difference?

We will find it out here.

What are Python and PySpark?

Before going forward with PySpark Vs Python we need to understand what they are.

Python

Python is becoming the most popular language for data scientists. Python helps you in utilizing your data abilities. Python is an extremely powerful language and easy to learn. Python is useful in data science, machine learning and artificial intelligence.

Python comprises various enticing characteristics. This includes ease of learning, simplified syntax, improved readability, and many more. The best part about Python is that it is both object-oriented as well as functional. It also allows programmers to think of code as both data and functionality.

PySpark

PySpark is a Python Spark API which is developed by the Apache Spark group to combine Python with Spark. PySpark helps in easy integration and manipulation of RDDs in Python. PySpark has an amazing reputation as a framework for working with huge amounts of datasets. Data Engineers love PySpark and utilize it to do computations on massive datasets.

There is a lot of difference between Python Vs PySpark. Let’s understand the advantages and disadvantages of both Python and PySpark.

Advantages of using Python

Multipurpose and Simple to use- Python is flexible, well-structured. and simple to use. Python allows you to use the advantages of many programming paradigms. It is object oriented yet incorporates aspects of functional programming.
It is Open-source- Python is an open-source language. You can create codes without paying anything. Also, the Python community is one of the largest communities globally.
Productivity- Python is an amazingly productive language. Its integration features allows Python to run efficiently.

Disadvantages of Python

Limitations on Speed- Python is an interpreted language. It is often slower than other programming languages.
Consumption of Memory- Python consumes a lot of RAM. It might be challenging to use Python if there are a large number of active items in RAM.
Not adaptive for Mobiles- Python is not suitable for mobile environments. It is not a great choice when it comes to mobile computing.

Advantages of PySpark

Swift Processing- PySpark will help you in obtaining faster performance on the disk. It is generally 10 times faster. It also offers 100 times faster in-memory performance.
Natural Dynamics- PySpark has 80 high-level operators. They will help you in creating a parallel application.
Fault-tolerant- PySpark allows the use of RDD for fault tolerance.

Disadvantages of PySpark

- Hard to express- PySpark is generally considered hard.
- Under-efficient- Compared to other programming it is less-efficient as compared to other models.
- Slow- Python is slow as compared to Scala when it comes to performance.