Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines
by: Alan Beardo Palacio (Author)
Publisher: Packt Publishing
Publication Date: 2021/5/25
Language: English
Print Length: 414 pages
ISBN-10: 183864721X
ISBN-13: 9781838647216
Book Description
Quickly build and deploy massive data pipelines and improve productivity using Azure DatabricksKey Features: Get to grips with the distributed training and deployment of machine leaing and deep leaing modelsLea how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep leaing and machine leaing models in a distributed computing infrastructureBook Description: Microsoft Azure Databricks helps you to haess the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine leaing and deep leaing models. Databricks’ advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines.The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks.Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.What You Will Lea: Create ETLs for big data in Azure DatabricksTrain, manage, and deploy machine leaing and deep leaing modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep leaingFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is for: This book is for software engineers, machine leaing engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to lea the concepts covered in this book more effectively. A basic understanding of machine leaing concepts and beginner-level Python programming knowledge is also recommended.
About the Author
Quickly build and deploy massive data pipelines and improve productivity using Azure DatabricksKey Features: Get to grips with the distributed training and deployment of machine leaing and deep leaing modelsLea how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep leaing and machine leaing models in a distributed computing infrastructureBook Description: Microsoft Azure Databricks helps you to haess the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine leaing and deep leaing models. Databricks’ advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines.The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks.Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.What You Will Lea: Create ETLs for big data in Azure DatabricksTrain, manage, and deploy machine leaing and deep leaing modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep leaingFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is for: This book is for software engineers, machine leaing engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to lea the concepts covered in this book more effectively. A basic understanding of machine leaing concepts and beginner-level Python programming knowledge is also recommended.