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PySpark SQL Recipeswith HiveQL, Data...

Mishra, Raju Kumar.

PySpark SQL Recipeswith HiveQL, Dataframe and Graphframes /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	PySpark SQL Recipesby Raju Kumar Mishra, Sundar Rajan Raman.
其他題名:	with HiveQL, Dataframe and Graphframes /
作者:	Mishra, Raju Kumar.
其他作者:	Raman, Sundar Rajan.
出版者:	Berkeley, CA :Apress :2019.
面頁冊數:	xxiv, 323 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Python (Computer program language)
電子資源:	https://doi.org/10.1007/978-1-4842-4335-0
ISBN:	9781484243350$q(electronic bk.)

PySpark SQL Recipeswith 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$q(electronic bk.)

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

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

PySpark SQL Recipeswith HiveQL, Dataframe and Graphframes /
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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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