Learn PySpark

1. Front Matter

   Pages i-xviii

   PDF

2. Introduction to Spark

   • Pramod Singh

   Pages 1-16

3. Data Processing

   • Pramod Singh

   Pages 17-48

4. Spark Structured Streaming

   • Pramod Singh

   Pages 49-65

5. Airflow

   • Pramod Singh

   Pages 67-84

6. MLlib: Machine Learning Library

   • Pramod Singh

   Pages 85-115

7. Supervised Machine Learning

   • Pramod Singh

   Pages 117-159

8. Unsupervised Machine Learning

   • Pramod Singh

   Pages 161-181

9. Deep Learning Using PySpark

   • Pramod Singh

   Pages 183-203

10. Back Matter

    Pages 205-210

    PDF

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.
You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. 


You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.


What You'll Learn

• Develop pipelines for streaming data processing using PySpark 
  
• Build Machine Learning & Deep Learning models using PySpark latest offerings
  
• Use graph analytics using PySpark 
  
• Create Sequence Embeddings from Text data 
  

Who This Book is For 



Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

• PySpark
• Python
• Machine Learning
• Deep Learning
• Big Data
• Spark
• Data Processing
• AirFlow
• Supervised Machine Learning
• Unsupervised Machine Learning
• Graph Frames

• Bangalore, India

  Pramod Singh

Pramod Singh is currently a Manager (Data Science) at Publicis Sapient and working as data science lead for a project with Mercedes Benz. He has spent the last nine years working on multiple Data projects at SapientRazorfish, Infosys & Tally and has used traditional to advanced machine learning and deep learning techniques in multiple projects using R, Python, Spark and Tensorflow. Pramod has also been a regular speaker at major conferences in India and abroad and is currently authoring a couple of books on Deep Learning and AI techniques. He regularly conducts Data Science meetups at SapientRazorfish and presents webinars on Machine Learning and Artificial Intelligence. He lives in Bangalore with his wife and 2-year-old son. In his spare time, he enjoys coding, reading and watching football.

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