Overview of PySpark

PySpark is an interface for Apache Spark written in Python, which allows users to write and run Spark applications using Python APIs parallelly on the distributed cluster (multiple nodes). In other words, PySpark is a Python API for Apache Spark. Spark was mainly written in Scala, and to support Python, PySpark was released for Python using Py4J, a Java library that allows Python to dynamically interface with JMV objects. For this reason, PySpark requires Java to be installed along with Python and Apache Spark. PySpark provides a rich set of tools and libraries, including MLlib for machine learning, Spark Streaming for real-time data processing, and GraphX for graph processing. These tools and libraries enable PySpark users to solve complex big data problems and perform advanced data analysis.

PySpark features

• In-memory computation: PySpark stores data in memory, allowing for faster data processing compared to disk-based processing. In general, Spark is considered to be ten times quicker than HDFS in processing big data.
• Lazy evaluation: PySpark uses a lazy evaluation model, which means it doesn’t execute transformations until an action is called. This helps optimize performance by reducing unnecessary computations.
• Inbuilt optimization: PySpark has several built-in optimization techniques to improve the performance of data processing.
• Cache and persistence: PySpark allows for caching and persistence of

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