PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
Date: March 18th, 2019
ISBN: 148424334X
Language: English
Number of pages: 348 pages
Format: EPUB
Add favorites
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.
What You Will Learn
• 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
Who This Book Is ForData scientists, Python programmers, and SQL programmers.
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.
What You Will Learn
• 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
Who This Book Is ForData scientists, Python programmers, and SQL programmers.
Download PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
Similar books
Information
Users of Guests are not allowed to comment this publication.
Users of Guests are not allowed to comment this publication.