UID:
almahu_9948104404702882
Format:
XXIV, 323 p. 57 illus.
,
online resource.
ISBN:
9781484243350
Content:
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.
Note:
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.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9781484243343
Additional Edition:
Printed edition: ISBN 9781484243367
Language:
English
DOI:
10.1007/978-1-4842-4335-0
URL:
https://doi.org/10.1007/978-1-4842-4335-0
URL:
Volltext
(URL des Erstveröffentlichers)
