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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

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

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 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

Why subscribe?

20. Other Books You May Enjoy

Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning

In this part, Chapter 10 delves into data pipeline orchestration, focusing on seamless task coordination and failure handling. It introduces tools such as Apache Airflow, Argo, Databricks Workflows, and Azure Data Factory. Chapter 11 highlights the Spark UI’s significance in performance optimization, covering the basics, tuning, resource optimization, and data handling techniques such as skewing, indexing, and partitioning.

This part has the following chapters:

Chapter 10, Data Pipeline Orchestration
Chapter 11, Performance Tuning

The rest of the chapter is locked

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Authors (3)

Eric Tome

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

David Radford

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

Rupam Bhattacharjee

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

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