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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Understanding the Spark DataFrame API

DataFrames are the most commonly used Spark API. They are a special type of Dataset with a type of Row (that is, Dataset[Row]). The major difference between DataFrames and Datasets is that DataFrames are not strongly typed, hence, data types are not checked at compile time. Because of this, they are arguably easier to work with as they do not require you to provide any structure while defining them.

We do this by creating a DataFrame similar to how we created a Dataset:

val personDf: DataFrame = spark
.read
.format("parquet")
.load(personDataLocation)

This is the output in the Spark console:

Figure 3.8 – DataFrame with our person data in the Spark console

The main difference is that we are not required to specify a type while instantiating the DataFrame object or on spark.read. Now, let’s take a look at the Spark SQL module.