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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
Authors (3):
Eric Tome Eric Tome
Profile icon Eric Tome
Rupam Bhattacharjee Rupam Bhattacharjee
Profile icon Rupam Bhattacharjee
David Radford David Radford
Profile icon David Radford
View More author details

Table of Contents (21) Chapters

Preface Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
Chapter 1: Scala Essentials for Data Engineers Chapter 2: Environment Setup Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark
Chapter 3: An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL Chapter 4: Working with Databases Chapter 5: Object Stores and Data Lakes Chapter 6: Understanding Data Transformation Chapter 7: Data Profiling and Data Quality Part 3 – Software Engineering Best Practices for Data Engineering in Scala
Chapter 8: Test-Driven Development, Code Health, and Maintainability Chapter 9: CI/CD with GitHub Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning
Chapter 10: Data Pipeline Orchestration Chapter 11: Performance Tuning Part 5 – End-to-End Data Pipelines
Chapter 12: Building Batch Pipelines Using Spark and Scala Chapter 13: Building Streaming Pipelines Using Spark and Scala Index Other Books You May Enjoy

Summary

In this chapter, we looked at various tools and technologies that we will need in subsequent chapters. We started with how to create a development environment in the cloud and then went through the steps to install the necessary software locally. We looked at how to install sbt, as well as how to configure IDEs such as VS Code and IntelliJ IDEA to work with Scala. We briefly looked at Docker, which is one of the most popular container engines; if you do not have it installed locally, we highly recommend you do so. We covered configuring Spark in detail, but you also have the option of setting up a Spark cluster using Docker containers. We covered the steps to install MySQL, which we will use in our chapter on working with databases. We also covered MinIO, which is a high-performance, Simple Storage Service (S3)-compatible object storage solution.

This chapter’s purpose was to prepare the tooling required for the subsequent chapters. In the next chapter, we will look...

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