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

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Working with streaming sources

Apache Spark uses the structured streaming API to read from a large variety of streaming sources. This API is easy for developers to use because it is very interoperable with Spark’s batch API, so developers can reuse their knowledge across both use cases. The following are some examples of using Spark to read from Kafka and Kinesis and show the results in the console. The writeStream portion will be covered in more detail later in this section:

//KINESIS
val spark = SparkSession
 .builder()
 .appName("Spark SQL basic example")
 .master("local[*]")
 .getOrCreate()
var df = spark.readStream.format("kinesis")
 .option("streamName","scala-book")
 .option("region","us-east-1")
 .option("initialPosition","TRIM_HORIZON")
 .option("awsAccessKeyId",sys.env.getOrElse("AWS_ACCESS_KEY_ID",""))
 .option("awsSecretKey",sys.env...