What is PySpark?
PySpark is a Python API for Spark released by the Apache Spark community to support Python with Spark. Using PySpark, one can easily integrate and work with RDDs in Python programming language too. There are numerous features that make PySpark such an amazing framework when it comes to working with huge datasets. Whether it is to perform computations on large datasets or to just analyze them, Data Engineers are switching to this tool.

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Key Features of PySpark
Real-time computations: Because of the in-memory processing in the PySpark framework, it shows low latency.
Polyglot: The PySpark framework is compatible with various languages such as Scala, Java, Python, and R, which makes it one of the most preferable frameworks for processing huge datasets.
Caching and disk persistence: This framework provides powerful caching and great disk persistence.
Fast processing: The PySpark framework is way faster than other traditional frameworks for Big Data processing.
Works well with RDDs: Python programming language is dynamically typed, which helps when working with RDDs.
Why PySpark? The Need of PySpark
More solutions to deal with big data, better. But then, if you have to switch between tools to perform different types of operations on big data, then having a lot of tools to perform a lot of different tasks does not sound very appealing, does it?

It just sounds like a lot of hassle one has to go through to deal with huge datasets. Here came some scalable and flexible tools to crack big data and gain benefits from it. One of those amazing tools that help handle big data is Apache Spark.

Now, it’s no secret that Python is one of the most widely used programming languages among Data Scientists, Data Analysts, and many other IT experts. The reason for this could be that it is simple and has an interactive interface or it is a general-purpose language. Therefore, it is trusted by Data Science folks to perform data analysis, Machine Learning, and many more tasks on big data. So, it’s pretty obvious that combining Spark and Python would rock the world of big data, isn’t it?

That is exactly what the Apache Spark community did when they came up with a tool called PySpark, which is basically a Python API for Apache Spark.

PySpark: Apache Spark with Python
PySpark is considered an interface for Apache Spark in Python. Through PySpark, you can write applications by using Python APIs. This interface also allows you to use PySpark Shell to analyze data in a distributed environment interactively. Being able to analyze huge data sets is one of the most valuable technical skills these days, and this tutorial will bring you to one of the most used technologies, Apache Spark, combined with one of the most popular programming languages, Python, by learning about which you will be able to analyze huge datasets. Here are some of the most frequently asked questions about Spark with Python:

Which programming language is more beneficial over others when used with Spark?
How to integrate Python with Spark?
What are the basic operations and building blocks of Spark that can be done using PySpark?
Apache Spark Overview
Apache Spark, as you might have heard of it, is a general engine for Big Data analysis, processing, and computations. It provides several advantages over MapReduce: it is faster, easier to use, offers simplicity, and runs virtually everywhere. It has built-in tools for SQL, Machine Learning, and streaming which make it a very popular and one of the most asked tools in the IT industry. Spark is written in Scala. Apache Spark has APIs for Python, Scala, Java, and R, though the most used languages with Spark are the former two. In this tutorial, you will learn how to use Python API with Apache Spark.