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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “It specifies the structure of the data with fields such as device_id, country, event_type, and event_ts.”

A block of code is set as follows:

val updateSilver: DataFrame = bronzeData
     .select(from_json(col("value"), jsonSchema).alias("value"))
     .select(
       col("value.device_id"),
       col("value.country"),
       col("value.event_type"),
       col("value.event_ts")
     )
     .dropDuplicates("device_id", "country", "event_ts")

Any command-line input or output is written as follows:

argo submit my-first-workflow.yaml --serviceaccount=spark -n spark-app --watch

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “You can find your policy in Shared access policies in the left-side of the Azure Event Hubs service.”

Tips or important notes

Appear like this.

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