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. __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 For** Data scientists, Python programmers, and SQL programmers. Front Matter ....Pages i-xxiv Introduction to PySpark SQL (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 1-22 Installation (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 23-64 IO in PySpark SQL (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 65-100 Operations on PySpark SQL DataFrames (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 101-166 Data Merging and Data Aggregation Using PySparkSQL (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 167-206 SQL, NoSQL, and PySparkSQL (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 207-248 Optimizing PySpark SQL (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 249-274 Structured Streaming (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 275-295 GraphFrames (Raju Kumar Mishra, Sundar Rajan Raman)....Pages 297-315 Back Matter ....Pages 317-323