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Essential PySpark for Scalable Data Analytics

You're reading from  Essential PySpark for Scalable Data Analytics

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Pages 322 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sreeram Nudurupati Sreeram Nudurupati
Profile icon Sreeram Nudurupati

Table of Contents (19) Chapters

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer 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 13: Integrating External Tools with Spark SQL

Business intelligence (BI) refers to the capabilities that enable organizations to make informed, data-driven decisions. BI is a combination of data processing capabilities, data visualizations, business analytics, and a set of best practices that enable, refine, and streamline organizations' business processes by helping them in both strategic and tactical decision making. Organizations typically rely on specialist software called BI tools for their BI needs. BI tools combine strategy and technology to gather, analyze, and interpret data from various sources and provide business analytics about the past and present state of a business.

BI tools have traditionally relied on data warehouses as data sources and data processing engines. However, with the advent of big data and real-time data, BI tools have branched out to using data lakes and other new data storage and processing technologies as data sources. In this chapter...

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