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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

Concepts

Data Engineering

Authors (3):

Eric Tome

Rupam Bhattacharjee

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

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Technical requirements

This chapter is long and contains lots of examples to explain the concepts that are introduced. All of the examples are self-contained, and we encourage you to try them yourself as you move through the chapter. You will need a working Scala environment to run these examples.

You can choose to configure it by following the steps outlined in Chapter 2 or use an online Scala playground such as Scastie (https://scastie.scala-lang.org/). We will use Scala 2.12 as the language version.

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Data Engineering with Scala and Spark

Published in: Jan 2024 Publisher: Packt ISBN-13: 9781804612583

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Authors (3)

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

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

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

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