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Python Vs PySpark

Python Vs PySpark

March 30th, 2019

PySpark is an API written for using Python along with Spark framework. As we all know, Spark is a computational engine, that works with Big Data and Python is a programming language.

Why Python?

There are many languages that data scientists need to learn, in order to stay relevant to their field. Few of them are Python, Java, R, Scala. Python is emerging as the most popular language for data scientists. Learning Python can help you leverage your data skills and will definitely take you a long way. Python is such a strong language which is also easier to learn and use. It is not just the data science, there are a lot of other domains such as machine learning, artificial intelligence that make use of Python.

Python is such a strong language which has a lot of appealing features like easy to learn, simpler syntax, better readability, and the list continues. It is an interpreted, functional, procedural and object-oriented. The best part of Python is that is both object-oriented and functional oriented and this gives programmers a lot of flexibility and freedom to think about code as both data and functionality. In other words, any programmer would think about solving a problem by structuring data and/or by invoking actions. The object-oriented is about data structuring (in the form of objects) and functional oriented is about handling behaviors.

Why Spark?

If you want to work with Big Data and Data mining, just knowing python might not be enough. You will be working with any data frameworks like Hadoop or Spark, as a data computational framework will help you better in the efficient handling of data. Spark is replacing Hadoop, due to its speed and ease of use. Spark can still integrate with languages like Scala, Python, Java and so on. And for obvious reasons, Python is the best one for Big Data. This is where you need PySpark.

PySpark is nothing, but a Python API, so you can now work with both Python and Spark. To work with PySpark, you need to have basic knowledge of Python and Spark. PySpark is clearly a need for data scientists, who are not very comfortable working in Scala because Spark is basically written in Scala. If you have a python programmer who wants to work with RDDs without having to learn a new programming language, then PySpark is the only way.

Python vs PySpark



Interpreted programming language A tool to support python on Spark
Used in Artificial Intelligence, Machine Learning, Big Data and much more Specially used in Big Data
Pre-requisites : Basics of any programming knowledge will be an added advantage, but not mandatory Pre-requisites : Knowledge of Spark  and Python is needed.
Has a  standard library that supports a wide variety of functionalities like databases, automation, text processing, scientific computing. It uses a library called Py4j, an API written in Python
Licensed under Python Created and licensed under Apache Spark Foundation