Data Engineering with Scala and Spark
Data Engineering with Scala and Spark
Build streaming and batch pipelines that process massive amounts of data using Scala
About the Book
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.
About the Author
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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