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 graphframes and see how to optimize your PySpark SQL code.\nPySpark 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.\nOn 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.\nWhat You Will Learn\nUnderstand 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\nWho This Book Is For\nData scientists, Python programmers, and SQL programmers.\nFrom the Back Cover\nCarry 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.\nPySpark 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.\nOn 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.\nYou will:\nUnderstand 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