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 Goal: Reader will  understand about PySpark, PySparkSQL , Catalyst Optimizer, Project Tungsten and Hive

No of pages                   20-30

Sub -Topics

1.      PySpark

2.      PySparkSQL

3.      Hive

4.      Catalyst

5.      Project Tungsten

Chapter 2:  Some time with Installation

Chapter Goal: Learner will understand about installation of Spark, Hive, PostgreSQL, MySQL, MongoDB, Cassandra etc.

No of pages: 30 -40

Sub - Topics                 

1.       Installation Spark

2.      Installation Hive

3.      Installation MySQL

4.      Installation MongoDB

Chapter 3:  IO in PySparkSQL

Chapter Goal: This chapter will provide recipes to the reader, which will  enable them to create PySparkSQL DataFrame from different sources.

No of pages : 40-50

Sub - Topics:                

1.      Creating DataFrame from data.

2.      Reading csv file to create Dataframe

3.  Reading JSON file to create Dataframe.

4.  Saving  DataFrames to different formats.

Chapter 4 :  Operations on PySparkSQL DataFrames

Chapter Goal:               Reader will learn about data filtering, data manuipulation, data descriptive analysis , Dealing with missing value etc

No Of Pages ; 40 -50

1.      Data filtering

2.      Data manipulation

3.      Row and column manipulation

Chapter 5 :  Data Merging and Data Aggregation using PySparkSQL

Chapter Goal: Reader will learn about data merging and aggregation using PySparkSQL

1.      Data Merging

2.      Data aggregation

Chapter 6: SQL, NoSQL and PySparkSQL

Chapter Goal: Reader will learn to run SQL and HiveQL queries on Dataframe

No of pages: 30-40

Sub - Topics:

1. Running SQL on DataFrame

2. Running HiveQL Chapter 7: Structured Streaming

Chapter Goal:               Reader will understand about structured streaming

No of pages : 30-40

1.      Different type of modes.

2.      Data aggregation in structured streaming

3.      Different type of sources

Chapter 8 : Optimizing PySparkSQL

Chapter Goal:               Reader will learn about optimizing PySparkSQL

No Of pages  : 20-30

Optimizing PySparkSQL

Chapter 9 : GraphFrames

Chapter Goal:               Reader will understand about graph data analysis with Graphframes. 

No of pages : 30-40

1. GraphFrame Creation

1.      Page Rank

2.      Breadth First Search

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.