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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Preface

Every company today is a data company regardless of the industry. Innovative companies use data to analyze the past, predict what will happen, and react to what is happening now. Data engineers are some of the most critical employees at companies today. They are essential for collecting, cleaning, and maintaining trusted datasets that analysts, data scientists, and reporting tools use to provide insights.

This book will teach you to leverage the Scala programming language on the Spark framework and the latest cloud technologies to build continuous and triggered data pipelines. You will do this by setting up a data engineering environment for local development and scalable distributed cloud deployments, using data engineering best practices, test-driven development, and Continuous Integration/Continuous Delivery (CI/CD). You will also orchestrate and performance-tune your end-to-end pipelines to deliver data to your end users.