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Modern Data Engineering with Apache Spark: A Hands-On Guide for Building Mission-Critical Streaming Applications
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About Modern Data Engineering With Apache Spark: A
From the Back Cover Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow.Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes.Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production.What You Will LearnSimplify data transformation with Spark Pipelines and Spark SQLBridge data engineering with machine learningArchitect modular data pipeline applicationsBuild reusable application components and librariesContainerize your Spark applications for consistency and reliabilityUse Docker and Kubernetes to deploy your Spark applicationsSpeed up application experimentation using Apache Zeppelin and DockerUnderstand serializable structured data and data contractsHarness effective strategies for optimizing data in your data lakesBuild end-to-end Spark structured streaming applications using Redis and Apache KafkaEmbrace testing for your batch and streaming applicationsDeploy and monitor your Spark applications Product Description Intermediate-Advanced user level About the Author Scott Haines is a full stack engineer with a current focus on real-time, highly available, trustworthy analytics systems. He works at Twilio as a Principal Software Engineer on the Voice Insights team, where he helps drive Spark adoption, creates streaming pipeline architectures, and helps to architect and build out a massive stream and batch processing platform.Prior to Twilio, Scott worked writing the backend Java APIs for Yahoo Games as well as the real-time game ranking and ratings engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo working for Flurry Analytics where he wrote the alerts and notifications system for mobile devices.
