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

English | 2024 | ISBN: 978-1804612583 | 300 Pages | PDF, EPUB | 28 MB

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)

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