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Data Engineering with Scala and Spark

You're reading from   Data Engineering with Scala and Spark Build streaming and batch pipelines that process massive amounts of data using Scala

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804612583
Length 300 pages
Edition 1st Edition
Languages
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Authors (3):
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Rupam Bhattacharjee Rupam Bhattacharjee
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Rupam Bhattacharjee
David Radford David Radford
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David Radford
Eric Tome Eric Tome
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Eric Tome
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
2. Chapter 1: Scala Essentials for Data Engineers FREE CHAPTER 3. Chapter 2: Environment Setup 4. Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark
5. Chapter 3: An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL 6. Chapter 4: Working with Databases 7. Chapter 5: Object Stores and Data Lakes 8. Chapter 6: Understanding Data Transformation 9. Chapter 7: Data Profiling and Data Quality 10. Part 3 – Software Engineering Best Practices for Data Engineering in Scala
11. Chapter 8: Test-Driven Development, Code Health, and Maintainability 12. Chapter 9: CI/CD with GitHub 13. Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning
14. Chapter 10: Data Pipeline Orchestration 15. Chapter 11: Performance Tuning 16. Part 5 – End-to-End Data Pipelines
17. Chapter 12: Building Batch Pipelines Using Spark and Scala 18. Chapter 13: Building Streaming Pipelines Using Spark and Scala 19. Index 20. Other Books You May Enjoy

Summary

This was a long chapter and we covered a lot of topics. We started this chapter with a brief introduction to functional programming, looked at why it is useful, and reviewed examples of RT. We then looked at various language features and constructs, starting with classes, objects, and traits. We looked at HOFs, which are one of the fundamental building blocks of functional programming. We looked at polymorphic functions and saw how they enable us to write reusable code. Then, we looked at variance, which defines subtyping relationships between objects, took a detailed tour of pattern matching, and finally, ended with implicit conversion, which is a powerful language feature used in design patterns such as type classes.

In the next chapter, we are going to focus on setting up the environment, which will allow you to follow along with the rest of the chapters.

You have been reading a chapter from
Data Engineering with Scala and Spark
Published in: Jan 2024
Publisher: Packt
ISBN-13: 9781804612583
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