PySpark SQL Recipes

With HiveQL, Dataframe and Graphframes

Nonfiction, Computers, Database Management, General Computing, Programming
Cover of the book PySpark SQL Recipes by Raju Kumar Mishra, Sundar Rajan Raman, Apress
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Author: Raju Kumar Mishra, Sundar Rajan Raman ISBN: 9781484243350
Publisher: Apress Publication: March 18, 2019
Imprint: Apress Language: English
Author: Raju Kumar Mishra, Sundar Rajan Raman
ISBN: 9781484243350
Publisher: Apress
Publication: March 18, 2019
Imprint: Apress
Language: English

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.

What You Will Learn

  • 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

Who This Book Is For

Data scientists, Python programmers, and SQL programmers.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

What You Will Learn

Who This Book Is For

Data scientists, Python programmers, and SQL programmers.

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