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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
Author Profile Icon Rupam Bhattacharjee
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

Environment Setup

In this chapter, we will outline two different environments for developing data engineering pipelines.

The first environment will use cloud-based tooling and services and will require no local environment setup. This is beneficial for multiple reasons. First, this type of environment is highly portable because you will be able to access it from any machine and any location as long as you have an internet connection and a browser. Second, it requires the least amount of setup to get started. The downside to this type of environment is that there are costs associated with another organization maintaining the systems you will be using for development.

The second environment will utilize your local machine to develop your pipeline code. This moves all the work of setting up environments to you but avoids the cloud costs mentioned earlier.

We will be covering the following topics in this chapter:

  • Setting up a cloud environment
  • Local environment setup...
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