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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

Ingesting data into data sinks

A data sink, as its name suggests, is a storage layer for storing raw or processed data either for short-term staging or long-term persistent storage. Though the term of data sink is commonly used in real-time data processing, there is no specific harm in calling any storage layer where ingested data lands a data sink. Just like data sources, there are also different types of data sinks. You will learn about a few of the most common ones in the following sections.

Ingesting into data warehouses

Data warehouses are a specific type of persistent data storage most prominent in Business Intelligence type workloads. There is an entire field of study dedicated to Business Intelligence and data warehousing. Typically, a data warehouse uses an RDBMS as its data store. However, a data warehouse is different from a traditional database in that it follows a specific type of data modeling technique, called dimensional modeling. Dimensional models are very intuitive...

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