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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

Chapter 2: Data Ingestion

Data ingestion is the process of moving data from disparate operational systems to a central location such as a data warehouse or a data lake to be processed and made conducive for data analytics. It is the first step of the data analytics process and is necessary for creating centrally accessible, persistent storage, where data engineers, data scientists, and data analysts can access, process, and analyze data to generate business analytics.

You will be introduced to the capabilities of Apache Spark as a data ingestion engine for both batch and real-time processing. Various data sources supported by Apache Spark and how to access them using Spark's DataFrame interface will be presented.

Additionally, you will learn how to use Apache Spark's built-in functions to access data from external data sources, such as a Relational Database Management System (RDBMS), and message queues such as Apache Kafka, and ingest them into data lakes. The different...

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