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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

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

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

Real-time analytics industry use cases

There is an actual need for and an advantage to processing data in real time, so companies are quickly shifting from batch processing to real-time data processing. In this section, let's take a look at a few examples of real-time data analytics by industry verticals.

Real-time predictive analytics in manufacturing

With the advent of the Internet of Things (IoT), manufacturing and other industries are generating a high volume of IoT data from their machines and heavy equipment. This data can be leveraged in few different ways to improve the way industries work and help them to save costs. One such example is predictive maintenance, where IoT data is continuously ingested from industrial equipment and machinery, data science, and machine learning techniques that have been applied to the data to identify patterns that can predict equipment or part failures. When this process is performed in real time, it can help to predict equipment and...

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