What is PySpark? Python vs PySpark

I am learing data engineering and PySpark is one of the main parts of it. I saw that on the internet there are no available good resources about PySpark so I thought why not share my explanation about PySpark with simple words. in this post I have explained all doubts that came to my mind when I heard first-time PySpark.

PySpark is a library of Python that is used for big data processing and analysis. It is built on top of Apache Spark, an open-source, big data processing framework that can handle large-scale data processing tasks. PySpark provides an API for developers to write applications that process and analyze large datasets in parallel across multiple servers.

In addition to data processing, PySpark also supports machine learning algorithms and graph processing, making it a versatile tool for data scientists and data engineers. It is widely used in the industry for data analysis, data warehousing, and data exploration. With its easy-to-use API and the popularity of Python, PySpark has become a popular choice for big data processing tasks. Whether you are a data scientist or engineer. PySpark can provide you with the tools you need to process and analyze large datasets efficiently and effectively.

What is the difference between PySpark and Python?


1. is a general-purpose programming language.
2. it uses internal memory and non-objective memory.
3. it is capable of processing real-time data.
4. it can be used for web development, data processing, app development, and data analysis.


1. it is a Python API for Apache Spark.
2. it is memory computing.
3. it is used to process bigger datasets in a distributed manner.
4. PySpark is mostly used by data engineers, machine learning engineers, and data scientists.

Is Pyspark a SQL? 

PySpark is not SQL, it is a Python library that builds for working with Apache Spark. another hand SQL(Structured Query Language) is used to manipulate data in databases like MySQL, and PL/SQL, PostgreSQL.

PySpark has a Spark SQL Named library that allows you to use SQL queries to extract, transform, and manipulate structured data stored in Spark DataFrames. now, it can come to your mind

what is Spark DataFrame? Spark DataFrames are like distributed tables that allow you to store and process large amounts of structured data in a parallel and efficient manner. 

So while PySpark itself is not SQL, it provides support for working with structured data using SQL, making it a powerful tool for big data processing and analysis.

What is the difference between Pyspark and Spark SQL? 

PySpark and Spark SQL are both components of Apache Spark, but they serve different purposes.

PySpark is a library for Python that provides an API for Apache Spark, making it easier for Python developers to use Spark for big data processing and analysis. PySpark provides distributed data processing, machine learning, and graph processing, as well as a convenient API for working with Spark DataFrames, Spark SQL, and Spark Streaming. 

on the other hand, Spark SQL is a library within Apache Spark that provides a SQL interface for working with structured data stored in Spark DataFrames. Spark SQL allows you to query structured data using SQL, as well as the Spark DataFrame API. With Spark SQL, you can perform operations such as filtering, aggregating, and joining data stored in Spark DataFrames.


I am assuming you understand what is PySaprk and how it is different from Python. guys if you are coming Into the field of data engineering then this type of doubt comes to mind. when I started learning data engineering then these doubts come to mind so try to explain to you in easy words.

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