Big Data Processing & Recovery in Python | Py_Spark & Python | Chapter 18
What is PySpark ?
Apache Spark is a powerful open-source data processing engine written in Scala, designed for large-scale data processing.
PySpark is the Python API for Apache Spark, an open source, distributed computing framework and set of libraries for real-time, large-scale data processing.
PySpark is a Python API that allows users to interact with Apache Spark:
What it does
PySpark enables users to write Python and SQL-like commands to analyze and manipulate data in a distributed processing environment.
Features
PySpark includes features such as--
Real-time computing: PySpark is designed for fast processing of large-scale data sets.
Disk and memory caching: PySpark includes features to help improve the performance of data
processing tasks.
Fault tolerance: PySpark has the capability to recover loss after a failure occurs.
PySparkSQL: A library that applies SQL-like analysis to structured or semi-structured data.
MLlib: A wrapper over PySpark and Spark's machine learning (ML) library.
GraphFrames: A graph processing library that provides a set of APIs for performing graph analysis efficiently.
Who uses it
PySpark is used by companies that collect terabytes of data. It's faster than libraries like Pandas and Dask, and can handle larger amounts of data.
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