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

PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes

Autor Raju Kumar Mishra, Sundar Rajan Raman
en Limba Engleză Paperback – 19 mar 2019
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.




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Specificații

ISBN-13: 9781484243343
ISBN-10: 148424334X
Pagini: 245
Ilustrații: XXIV, 323 p. 57 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.49 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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 advancedfeatures
  • Use SQL and HiveQL with PySpark SQL
  • Work with structured streaming
  • Optimize PySpark SQL 
  • Master graphframes and graph processing

Caracteristici

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