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PySpark SQL Recipes

With HiveQL, Dataframe and Graphframes

  • Raju Kumar Mishra
  • Sundar Rajan Raman

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 1-22
  3. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 23-64
  4. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 65-100
  5. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 101-166
  6. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 167-206
  7. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 207-248
  8. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 249-274
  9. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 275-295
  10. Raju Kumar Mishra, Sundar Rajan Raman
    Pages 297-315
  11. Back Matter
    Pages 317-323

About this book

Introduction

Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.

PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes.

On completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.

You will:

  • Understand PySpark SQL and its advanced features
  • Use SQL and HiveQL with PySpark SQL
  • Work with structured streaming
  • Optimize PySpark SQL 
  • Master graphframes and graph processing

Keywords

PySpark PySpark SQL NO SQL Graph frames Data Processing Spark Streaming Big Data Python

Authors and affiliations

  • Raju Kumar Mishra
    • 1
  • Sundar Rajan Raman
    • 2
  1. 1.BangaloreIndia
  2. 2.ChennaiIndia

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