Raju Kumar Mishra - PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
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- Book:PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
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PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes: summary, description and annotation
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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 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.
Raju Kumar Mishra: author's other books
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Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4334-3 . For more detailed information, please visit http://www.apress.com/source-code .
To the Almighty, who guides me in every aspect of my life. And to my mother, Smt. Savitri Mishra, and my lovely wife, Smt. Smita Rani Pathak.
Chapter , Introduction to PySparkSQL, covers Many Big Data processing tools such as Apache Hadoop, Apache Pig, Apache Hive, and Apache Spark. The shortcomings of Hadoop and the evolution of Spark are discussed. It discusses PySparkSQL, includes an introduction to DataFrame, and covers structured streaming. A discussion of Apache Kafka is also included. This chapter also sheds light on some NoSQL databases like MongoDB and Cassandra.
Chapter , Installation, will take you to the real battleground. Youll learn how to install many Big Data processing tools such as Hadoop, Hive, Spark, MongoDB, and Apache Cassandra.
Chapter , IO in PySparkSQL, will take you through many recipes that read data from many data sources using PySparkSQL. Youll read data from many file formats like CSV, JSON, ORC, and Parquet, then from many RDBMS like MySQL and PostgreSQL. It also discusses how to read data from NoSQL databases like MongoDB and Cassandra using PySparkSQL. Then you see how to save the data into many sinks like files and RDBMS or NoSQL databases.
Chapter , Operations on PySparkSQL DataFrames, explains different operations like data filtering, data transformation, and data sorting on DataFrames.
Chapter , Data Merging and Data Aggregation Using PySparkSQL, shows how to perform data aggregation and data merging on DataFrames.
Chapter , SQL, NoSQL, and PySparkSQL, shows how to perform SQL operations on DataFrames. It contains multiple recipes that will help you convert DataFrames to table-like structures and then apply SQL queries to them.
Chapter , Optimizing PySparkSQL, shows you how to perform optimal joins that run faster. You will understand the basics of how Spark works in the background and, based on that, you will see multiple recipes that will help you optimize your SQL queries on DataFrames.
Chapter , Structured Streaming, shows you how to use Spark streaming with streaming data. This chapter provides multiple recipes to help you apply Sparks structured streaming APIs and SQLs to streaming data.
Chapter , GraphFrames, shows you how to perform Graph operations on DataFrames. There are multiple GraphFrame recipes, including PageRank, that will help you to appreciate and apply complex graph operations using Sparks GraphFrame.
My heartiest thanks to the Almighty. I also would like to thank my mother, Smt. Savitri Mishra; my sisters, Mitan and Priya; my cousins, Suchitra and Chandni; and my maternal uncle, Shyam Bihari Pandey; for their support and encouragement. I am very grateful to my sweet and beautiful wife, Smt. Smita Rani Pathak, for her continuous encouragement and love while I was writing this book. I thank my brother-in-law, Mr. Prafull Chandra Pandey, for his encouragement to write this book. I am very thankful to my sisters-in-law, Rinky, Reena, Kshama, Charu, and Dhriti, for their encouragement as well. I am grateful to Anurag Pal Sehgal, Saurabh Gupta, Devendra Mani Tripathi, Avinash Dash, Rajesh Thakur, and all my friends. My nephews, Rashu and Rishu. Last but not least, thanks to coordinating editor Aditee Mirashi and acquisitions editor Celestin Suresh John at Apress; without them, this book would not have been possible.
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 the 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 teach and explain 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.
has been working as a Big Data architect with strong hands-on experience in various technologies such as Hadoop, Spark, Hive, Pig, oozie, Kafka, and others. With a strong Machine Learning background, he has implemented various Machine Learning projects that are based on huge volumes of data. Sundar completed his B.Tech from the National Institute of Technology with Honors. He has an innovative mind for solving complex problems. He also has patents in his name. He is currently working for one of the top Financial Institutions in the United States of America.
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