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

Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data

Key Features

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)

Book 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.

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

Who this book is 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.

Table of Contents

Scala Essentials for Data Engineers

Environment Setup

An Introduction to Apache Spark and Its APIs - DataFrame, Dataset, and Spark SQL

Working with Databases

Object Stores and Data Lakes

Understanding Data Transformation

Data Profiling and Data Quality

Test-Driven Development, Code Health, and Maintainability

CI/CD with GitHub

Data Pipeline Orchestration

Performance Tuning

Building Batch Pipelines Using Spark and Scala

Building Streaming Pipelines Using Spark and Scala

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