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

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. Spis treści: 1. Scala Essentials for Data Engineers2. Environment Setup3. An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL4. Working with Databases5. Object Stores and Data Lakes6. Understanding Data Transformation7. Data Profiling and Data Quality8. Test-Driven Development, Code Health, and Maintainability9. CI/CD with GitHub10. Data Pipeline Orchestration11. Performance Tuning12. Building Batch Pipelines Using Spark and Scala13. Building Streaming Pipelines Using Spark and Scala