PySpark SQL Recipes

With HiveQL, Dataframe and Graphframes
1. Auflage 2019
ISBN: 978-1-4842-4334-3
Verlag: Apress

Buch, Englisch, 323 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 528 g

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Raman, Sundar Rajan

Mishra, Raju Kumar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Betriebssysteme Linux Betriebssysteme, Open Source Betriebssysteme

Inhaltsverzeichnis

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.

Über Autor(innen)

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.