PySpark SQL Recipes With HiveQL, Dataframe and 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 gra...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Mishra, Raju Kumar (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Raman, Sundar Rajan (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2019.
Έκδοση:1st ed. 2019.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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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. 
250 |a 1st ed. 2019. 
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300 |a XXIV, 323 p. 57 illus.  |b online resource. 
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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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