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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

Consolidating data using data integration

Data integration is an important step in both the ETL and ELT modes of data processing. Data integration is the process of combining and blending data from different data sources to create enriched data that happens to represent a single version of the truth. Data integration is different from data ingestion because data ingestion simply collects data from disparate sources and brings it to a central location, such as a data warehouse. On the other hand, data integration combines those disparate data sources to create a meaningful unified version of the data that represents all the dimensions of the data. There are multiple ways to perform data integration, and a few of them will be explored in this section.

Data consolidation via ETL and data warehousing

Extracting, transforming, and loading data into data warehouses has been the best technique of data integration over the last few decades. One of the primary goals of data consolidation...

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