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Essential PySpark for Scalable Data Analytics

You're reading from Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781800568877

Length 322 pages

Edition 1st Edition

Languages

Python

Tools

PySpark

Concepts

Big Data

Author (1):

Sreeram Nudurupati

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Data Engineering

2. Chapter 1: Distributed Computing Primer FREE CHAPTER

3. Chapter 2: Data Ingestion

4. Chapter 3: Data Cleansing and Integration

5. Chapter 4: Real-Time Data Analytics

6. Section 2: Data Science

7. Chapter 5: Scalable Machine Learning with PySpark

8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection

9. Chapter 7: Supervised Machine Learning

10. Chapter 8: Unsupervised Machine Learning

11. Chapter 9: Machine Learning Life Cycle Management

12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark

13. Section 3: Data Analysis

14. Chapter 11: Data Visualization with PySpark

15. Chapter 12: Spark SQL Primer

16. Chapter 13: Integrating External Tools with Spark SQL

17. Chapter 14: The Data Lakehouse

18. Other Books You May Enjoy

Who this book is for

This book is intended for practicing data engineers, data scientists, data analysts, citizen data analysts, and data enthusiasts who are already using data analytics to delve into the world of distributed and scalable data analytics. It's recommended that you have knowledge of the field of data analytics and data manipulation to gain actionable insights.

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Authors (1)

Nudurupati

Nudurupati

Sreeram Nudurupati is a data analytics professional with years of experience in designing and optimizing data analytics pipelines at scale. He has a history of helping enterprises, as well as digital natives, build optimized analytics pipelines by using the knowledge of the organization, infrastructure environment, and current technologies.

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