Distributed Data Systems with Azure Databricks:Create, deploy, and manage enterprise data pipelines
by:Alan Bernardo Palacio
Publisher Finelybook 出版社：Packt Publishing (25 May 2021)
pages 页数：414 pages
Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks
Get to grips with the distributed training and deployment of machine learning and deep learning models
Learn how ETLs are integrated with Azure Data Factory and Delta Lake
Explore deep learning and machine learning models in a distributed computing infrastructure
Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning 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 inference using TensorFlow and TensorRT with the ResNet-50 model. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement Data Definition Language (DDL) 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 learn
Create ETLs for big data in Azure Databricks
Train, manage, and deploy machine learning and deep learning models
Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation
Discover how to use Horovod for distributed deep learning
Find out how to use Delta Engine to query and process data from Delta Lake
Understand how to use Data Factory in combination with Databricks
Use Structured Streaming in a production-like environment