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Data Engineering with Scala and Spark
Data Engineering with Scala and Spark

Data Engineering with Scala and Spark: Build streaming and batch pipelines that process massive amounts of data using Scala

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Profile Icon Eric Tome Profile Icon Rupam Bhattacharjee Profile Icon David Radford
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€19.99 €22.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (5 Ratings)
eBook Jan 2024 300 pages 1st Edition
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€19.99 €22.99
Paperback
€27.99
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Free Trial
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Arrow left icon
Profile Icon Eric Tome Profile Icon Rupam Bhattacharjee Profile Icon David Radford
Arrow right icon
€19.99 €22.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (5 Ratings)
eBook Jan 2024 300 pages 1st Edition
eBook
€19.99 €22.99
Paperback
€27.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€19.99 €22.99
Paperback
€27.99
Subscription
Free Trial
Renews at €18.99p/m

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Data Engineering with Scala and Spark

Scala Essentials for Data Engineers

Welcome to the world of data engineering with Scala. But why Scala? The following are some of the reasons for learning Scala:

  • Scala provides type safety
  • Big corporations such as Netflix and Airbnb have a lot of data pipelines written in Scala
  • Scala is native to Spark
  • Scala allows data engineers to adopt a software engineering mindset

Scala is a high-level general-purpose programming language that runs on a standard Java platform. It was created by Martin Odersky in 2001. The name Scala stands for scalable language, and it provides excellent support for both object-oriented and functional programming styles.

This chapter is meant as a quick introduction to concepts that the subsequent chapters build upon. Specifically, this chapter covers the following topics:

  • Understanding functional programming
  • Understanding objects, classes, and traits
  • Higher-order functions (HOFs)
  • Examples of HOFs from the Scala collection library
  • Understanding polymorphic functions
  • Variance
  • Option types
  • Collections
  • Pattern matching
  • Implicits in Scala
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Key benefits

  • Transform data into a clean and trusted source of information for your organization using Scala
  • Build streaming and batch-processing pipelines with step-by-step explanations
  • Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD)
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Most data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.

Who is this book for?

This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.

What you will learn

  • Set up your development environment to build pipelines in Scala
  • Get to grips with polymorphic functions, type parameterization, and Scala implicits
  • Use Spark DataFrames, Datasets, and Spark SQL with Scala
  • Read and write data to object stores
  • Profile and clean your data using Deequ
  • Performance tune your data pipelines using Scala

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2024
Length: 300 pages
Edition : 1st
Language : English
ISBN-13 : 9781804614327
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Product Details

Publication date : Jan 31, 2024
Length: 300 pages
Edition : 1st
Language : English
ISBN-13 : 9781804614327
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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(5 Ratings)
5 star 40%
4 star 40%
3 star 20%
2 star 0%
1 star 0%
Om S Mar 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In "Data Engineering with Scala and Spark," you'll embark on a journey to enhance your data engineering skills using Scala and functional programming techniques. The book focuses on creating continuous and scheduled pipelines for data ingestion, transformation, and aggregation.Key Features:Use Scala to transform data reliably.Learn to build streaming and batch-processing pipelines with clear explanations.Implement CI/CD best practices and test-driven development (TDD).The book covers essential topics like setting up development environments, working with Spark APIs (DataFrame, Dataset, and Spark SQL), data profiling, quality assurance, and pipeline orchestration. It also includes insights into performance tuning and best practices for building robust data pipelines.
Amazon Verified review Amazon
Zheng Zhu Feb 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Data Engineering with Scala and Spark" is a fantastic survey of the key concepts and practices in modern data engineering with Apache Spark and data lake architectures. I'm a data professional in the software industry and have been working with Apache Spark for close to a decade now, which is even prior to cloud data lakes and platforms like Databricks becoming mainstream. This book does a great job of establishing the foundational concepts with Scala and Spark in its first few chapters, which gives the reader the necessary tools to experiment and extend their knowledge. The progression of the book is easy to follow, which goes toward advanced transformations, data quality, and finally to best practice data engineering patterns. I very much respect its coverage of Spark with the Scala language, as it continues to be the native programming language of Spark itself, and one that has the deepest level of integration and best performance characteristics when it comes to data engineering.One concept I really appreciate from the author in this book is its coverage, albeit somewhat brief, of Test Driven Development and CI/CD. The data engineering industry, in my opinion, has yet to fully adopt and institute the degree of rigor and engineering disciplines that are now pervasive with general software engineering in both backend and frontend settings. As a result, data pipelines of any real complexity for large organizations eventually become very brittle, difficult to manage, and costly to operate. This book plants a great seed in the mind of its readers that these concepts around unit and integration testing via CI/CD with data pipelines are best practices for data engineering and a necessary knowledge area for data engineers in our current environment. I would loved to have seen some concrete samples of full integration tests that tests the logic of Spark transformations, which is an essential practice that typical Spark engineers lack familiarity with.In the concluding parts of the book, the author covers areas on orchestration, performance tuning, and end-to-end pipelines for both batch and streaming modalities. These are deep and advanced concepts, and there certainly can be full books written on each of these topics just by themselves. I like the broad coverage of several orchestration frameworks, giving the users an unbiased perspective on how tools like Airflow, Databricks Workflows, and ADF can be used with Spark. I also support the judicious coverage of some of the key concepts in Spark performance tuning, including data skew, partitioning, and right-sizing compute, which are generally the most important concepts to understand when tuning pipelines.Overall, I recommend this book for readers seeking to gain a deeper level of understanding of what data engineering is about and how to best achieve that with Apache Spark, in addition to the current set of companion platforms and tooling in the data engineering ecosystem. The reader should expect to be able to construct and support cloud-based or local data pipelines from various source modalities with Apache Spark in an end-to-end fashion, which I think makes this book a worthwhile journey.
Amazon Verified review Amazon
Loni Apr 04, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
"Data Engineering with Scala and Spark" offers a comprehensive guide to navigating the complexities of Apache Spark and modern data engineering practices. From fundamental concepts to advanced optimization techniques, each chapter provides clear explanations and practical insights for building efficient data pipelines. With a focus on real-world applications and best practices, this book is essential reading for data engineers and professionals seeking to harness the full potential of Apache Spark in their projects.
Amazon Verified review Amazon
H2N Apr 01, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
A good resource for who looking to master Scala, Spark, and cloud computing for data engineering. The book covers essential concepts and best practices, it guides readers through setting up environments, developing pipelines, and applying test-driven development and CI/CD and also advanced topics like data transformation, quality checks, and performance tuning with practical examples. Overall, it's a highly valuable resource for anyone aspiring to excel in data engineering.
Amazon Verified review Amazon
fernando Feb 09, 2024
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This is a book for a newbie. If you have experience you won’t learn much from it.
Amazon Verified review Amazon
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