It's crucial to understand why and when to use Spark with Python if you're going to learn PySpark. Most of Spark's functionality, including Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core, are supported by PySpark. It enables you to create Spark applications using Python APIs and gives you access to the PySpark shell, enabling interactive data analysis in a distributed setting. Python's PySpark provides an interface for Apache Spark. It enables Python users to work with Resilient Distributed Datasets (RDDs). Pyspark is a tool developed by Apache Spark Community for integrating Python with Spark. So now that we understand Apache Spark, it will be easier for us to understand PySpark. It is very popular and one of the most requested tools in the IT industry because it has in-built tools for SQL, machine learning (ML), and streaming. Spark is written in Scala, but it can also be used from Python using Pyspark. Apache Spark is an open-source cluster computing framework that is used to develop big data applications that can perform fast analytics over large data sets. To understand Pyspark and its use in the big data world, we must first understand Apache Spark.
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