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Guide Pratique de PySpark pour Data Engineer: Fonctions Usuelles et Exemples d’Applications
[Practical Guide of PySpark for Data Engineer: Common Functions and Application Examples]

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  • Keita, Moussa

Abstract

The area of Big Data is commonly characterized by situations where the volumes of data are such that it is impossible to store and process them on a single machine. Data are stored across a group of machines called "cluster". However, new technological solutions had to be imagined by IT engineers in order to be able to process and exploit the data distributed across a cluster. Apache Spark is one of the proposed solutions. Spark is an framework that allows applying parallel computations to data stored on several cluster nodes. PySpark is the implementation of the Spark framework in the Python programming language. The purpose of this document is to review the common parallel processing functions used by Big Data Engineers using PySpark.

Suggested Citation

  • Keita, Moussa, 2022. "Guide Pratique de PySpark pour Data Engineer: Fonctions Usuelles et Exemples d’Applications [Practical Guide of PySpark for Data Engineer: Common Functions and Application Examples]," MPRA Paper 113562, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:113562
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    More about this item

    Keywords

    RDD; Dataframe; Big Data; PySpark; Hive; HDFS; csv; kafka;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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