Take your data engineering skills to the next level by learning to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Performance in distributed computing environments is a critical factor for data engineers. If performance in a distributed computing environment is not optimal, several consequences can arise like slow data processing, bottlenecks and latency, inefficient resource utilization, etc. 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 to leverage the Scala programming language on the Spark framework and the latest cloud technologies to build continuous and triggered data pipelines. You will 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 will then understand DataFrame API, Dataset API, and Spark SQL API and its use. You will further learn about data profiling and quality in Scala. You will also orchestrate and performance-tune 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. This book is aimed at data engineers who are experienced in working with data but 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.

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

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