Distributed Data Systems with Azure Databricks

This book helps you to learn how to extract, transform, and orchestrate massive amounts of data to develop robust data pipelines. You'll perform complex machine learning tasks using advanced Azure Databricks features, and also explore model tuning, deployment, and control using Databricks functionalities such as AutoML and Delta Lake with ...

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Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks

Key Features

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

Book DescriptionMicrosoft 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 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 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

Who this book is forThis book is for software engineers, machine learning 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 learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.