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.

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Spis treści

Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala eBook -- spis treści

• 1. Scala Essentials for Data Engineers
• 2. Environment Setup
• 3. An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL
• 4. Working with Databases
• 5. Object Stores and Data Lakes
• 6. Understanding Data Transformation
• 7. Data Profiling and Data Quality
• 8. Test-Driven Development, Code Health, and Maintainability
• 9. CI/CD with GitHub
• 10. Data Pipeline Orchestration
• 11. Performance Tuning
• 12. Building Batch Pipelines Using Spark and Scala
• 13. Building Streaming Pipelines Using Spark and Scala