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PySpark SQL Recipes : with HiveQL, Dataframe and Graphframes

Author: Raju Kumar Mishra; Sundar Rajan Raman
Publisher: [Berkeley, California] : Apress, [2019]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Raju Kumar Mishra; Sundar Rajan Raman
ISBN: 9781484243350 1484243358 9781484243367 1484243366
OCLC Number: 1090539417
Description: 1 online resource
Contents: Introduction to PySpark SQL --
Installation --
10 in PySpark SQL --
Operations on PySpark SQL DataFrames --
Data merging and data aggregation using PySparkSQL --
SQL, NoSQL, and PySparkSQL --
Optimizing PySpark SQL --
Structured streaming --
GraphFrames.
Responsibility: Raju Kumar Mishra and Sundar Rajan Raman.

Abstract:

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.

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