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Data Engineering with Scala and Spark

You're reading from Data Engineering with Scala and Spark

Product type Book

Published in Jan 2024

Publisher Packt

ISBN-13 9781804612583

Pages 300 pages

Edition 1st Edition

Languages

Concepts

Data Engineering

Authors (3):

Eric Tome

Rupam Bhattacharjee

David Radford

View More author details

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

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

Why subscribe?

20. Other Books You May Enjoy

Further reading

Programming in Scala, Fourth Edition, Martin Odersky, Lex Spoon, and Bill Venners
If you are interested in learning more about functional programming, please refer to Functional Programming in Scala by Paul Chiusano and Rúnar Bjarnaso
A nice explanation of how app traits actually work
https://stackoverflow.com/questions/53468358/how-Scala-app-trait-and-main-works-internally
Scala’s stackable trait pattern by Bill Venners: https://www.artima.com/articles/Scalas-stackable-trait-pattern
Please refer to the Scala docs for more details on the collection library: https://docs.scala-lang.org/overviews/collections/overview.html

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Data Engineering with Scala and Spark

Published in: Jan 2024 Publisher: Packt ISBN-13: 9781804612583

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Authors (3)

Eric Tome

Eric Tome has over 25 years of experience working with data. He has contributed to and led teams that ingested, cleansed, standardized, and prepared data used by business intelligence, data science, and operations teams. He has a background in mathematics and currently works as a senior solutions architect at Databricks, helping customers solve their data and AI challenges.

See other products by Eric Tome

Rupam Bhattacharjee

Rupam Bhattacharjee works as a lead data engineer at IBM. He has architected and developed data pipelines, processing massive structured and unstructured data using Spark and Scala for on-premises Hadoop and K8s clusters on the public cloud. He has a degree in electrical engineering.

See other products by Rupam Bhattacharjee

David Radford

David Radford has worked in big data for over 10 years, with a focus on cloud technologies. He led consulting teams for several years, completing a migration from legacy systems to modern data stacks. He holds a master's degree in computer science and works as a senior solutions architect at Databricks.

See other products by David Radford

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