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PySpark SQL Recipes = with HiveQL, D...

Mishra, Raju Kumar.

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PySpark SQL Recipes = with HiveQL, Dataframe and Graphframes /

Record Type:	Electronic resources : Monograph/item
Title/Author:	PySpark SQL Recipes/ by Raju Kumar Mishra, Sundar Rajan Raman.
Reminder of title:	with HiveQL, Dataframe and Graphframes /
Author:	Mishra, Raju Kumar.
other author:	Raman, Sundar Rajan.
Published:	Berkeley, CA :Apress : : 2019.,
Description:	xxiv, 323 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to PySparkSQL -- Chapter 2: Some time with Installation -- Chapter 3: IO in PySparkSQL -- Chapter 4 : Operations on PySparkSQL DataFrames -- Chapter 5 : Data Merging and Data Aggregation using PySparkSQL -- Chapter 6: SQL, NoSQL and PySparkSQL -- Chapter 7: Structured Streaming -- Chapter 8 : Optimizing PySparkSQL -- Chapter 9 : GraphFrames.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-4335-0
ISBN:	9781484243350

PySpark SQL Recipes = with HiveQL, Dataframe and Graphframes /
Mishra, Raju Kumar.

PySpark SQL Recipeswith HiveQL, Dataframe and Graphframes /[electronic resource] :by Raju Kumar Mishra, Sundar Rajan Raman. - Berkeley, CA :Apress :2019. - xxiv, 323 p. :ill., digital ;24 cm.

Chapter 1: Introduction to PySparkSQL -- Chapter 2: Some time with Installation -- Chapter 3: IO in PySparkSQL -- Chapter 4 : Operations on PySparkSQL DataFrames -- Chapter 5 : Data Merging and Data Aggregation using PySparkSQL -- Chapter 6: SQL, NoSQL and PySparkSQL -- Chapter 7: Structured Streaming -- Chapter 8 : Optimizing PySparkSQL -- Chapter 9 : GraphFrames.

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.

ISBN: 9781484243350

Standard No.: 10.1007/978-1-4842-4335-0doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

PySpark SQL Recipes = with HiveQL, Dataframe and Graphframes /
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245 1 0 $a PySpark SQL Recipes $h [electronic resource] : $b with HiveQL, Dataframe and Graphframes / $c by Raju Kumar Mishra, Sundar Rajan Raman.
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505 0 $a Chapter 1: Introduction to PySparkSQL -- Chapter 2: Some time with Installation -- Chapter 3: IO in PySparkSQL -- Chapter 4 : Operations on PySparkSQL DataFrames -- Chapter 5 : Data Merging and Data Aggregation using PySparkSQL -- Chapter 6: SQL, NoSQL and PySparkSQL -- Chapter 7: Structured Streaming -- Chapter 8 : Optimizing PySparkSQL -- Chapter 9 : GraphFrames.
520 $a 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.
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