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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Summary

In this chapter, we looked at various tools and technologies that we will need in subsequent chapters. We started with how to create a development environment in the cloud and then went through the steps to install the necessary software locally. We looked at how to install sbt, as well as how to configure IDEs such as VS Code and IntelliJ IDEA to work with Scala. We briefly looked at Docker, which is one of the most popular container engines; if you do not have it installed locally, we highly recommend you do so. We covered configuring Spark in detail, but you also have the option of setting up a Spark cluster using Docker containers. We covered the steps to install MySQL, which we will use in our chapter on working with databases. We also covered MinIO, which is a high-performance, Simple Storage Service (S3)-compatible object storage solution.

This chapter’s purpose was to prepare the tooling required for the subsequent chapters. In the next chapter, we will look...

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

Table of Contents (21) Chapters

Summary

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