The book offers a practical approach to addressing common programming challenges in big data processing. Organized in a problem-solution format, it allows readers to easily locate specific programming issues and implement straightforward solutions into their own code. This hands-on guide is designed for quick reference, enabling efficient problem-solving for programmers dealing with big data scenarios.
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.
This volume provides an overview of cognitive science and critically assess areas within the topic that are evolving rapidly. Using multidisciplinary studies and rich empirical literature, discussions and demonstrations, this book highlights emerging domains and novel themes within cognitive science.