PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes by Raju Kumar Mishra

Description: PySpark SQL Recipes by Raju Kumar Mishra, Sundar Rajan Raman 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. Youll also discover how to solve problems in graph analysis using graphframes.On completing this book, youll 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 LearnUnderstand PySpark SQL and its advanced featuresUse SQL and HiveQL with PySpark SQLWork with structured streamingOptimize PySpark SQL Master graphframes and graph processingWho This Book Is ForData scientists, Python programmers, and SQL programmers. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover 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. Youll also discover how to solve problems in graph analysis using graphframes. On completing this book, youll 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 Author Biography Raju Kumar Mishra has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others. His venture Walsoul Private Ltd provides training in data science, programming, and big data.Sundar Rajan Raman is an artificial intelligence practitioner currently working at Bank of America. He holds a Bachelor of Technology degree from the National Institute of Technology, India. Being a seasoned Java and J2EE programmer he has worked on critical applications for companies such as AT&T, Singtel, and Deutsche Bank. He is also a seasoned big data architect. His current focus is on artificial intelligence space including machine learning and deep learning. Table of Contents 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. Feature Explains PySpark SQL and Dataframe in detail Include IO operation using PySpark SQL from most frequently used SQL and NoSQL databases Detail discussion on Data Preprocessing using PySpark SQL Problem Solution approach to graph bases algorithm using Graphframes Details ISBN148424334X Author Sundar Rajan Raman Year 2019 ISBN-10 148424334X ISBN-13 9781484243343 Edition 1st Format Paperback Imprint APress Subtitle With HiveQL, Dataframe and Graphframes Place of Publication Berkley Country of Publication United States Pages 323 Publication Date 2019-03-19 DEWEY 005.133 Short Title PySpark SQL Recipes Language English DOI 10.1007/978-1-4842-4335-0 AU Release Date 2019-03-19 NZ Release Date 2019-03-19 US Release Date 2019-03-19 UK Release Date 2019-03-19 Illustrations 57 Illustrations, black and white; XXIV, 323 p. 57 illus. Publisher APress Edition Description 1st ed. 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