Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.

Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You''ll also uncover how ACID transactions bring reliability to data lakehouses at scale.

This book helps you:

Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.

Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You''ll also uncover how ACID transactions bring reliability to data lakehouses at scale.

This book helps you:

Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale.This book helps you:Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

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 compilereusable 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 contracts Harness 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 Who This Book Is ForProfessional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness anduse Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world

Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.

Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You''ll also uncover how ACID transactions bring reliability to data lakehouses at scale.

This book helps you:

Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.

Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You''ll also uncover how ACID transactions bring reliability to data lakehouses at scale.

This book helps you:

Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques.Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale.This book helps you:Understand key data reliability challenges and how Delta Lake solves themExplain the critical role of Delta transaction logs as a single source of truthLearn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and TrinoArchitect data lakehouses with the medallion architectureOptimize Delta Lake performance with features like deletion vectors and liquid clustering

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 compilereusable 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 contracts Harness 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 Who This Book Is ForProfessional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness anduse Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world