PySpark SQL recipes : with HiveQL dataframe and graphframes 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