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

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781804612583

Length 300 pages

Edition 1st Edition

Languages

Scala

Concepts

Data Engineering

Authors (3):

Rupam Bhattacharjee

David Radford

Eric Tome

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Table of Contents (21) Chapters

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

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Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark

In this part, Chapter 3 introduces Apache Spark as a scalable data processing framework, covering its basics, Scala application development, and the Dataset/DataFrame APIs. Chapter 4 explores relational databases in data pipelines, highlighting Spark’s JDBC API. Chapter 5 discusses the rise of data lakes and lake houses, while Chapter 6 delves into advanced Spark data transformation. Chapter 7 focuses on data quality with the Deequ library for checks and metrics.

This part has the following chapters:

Chapter 3, An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL
Chapter 4, Working with Databases
Chapter 5, Object Stores and Data Lakes
Chapter 6, Understanding Data Transformation
Chapter 7, Data Profiling and Data Quality