Book Image

Distributed Data Systems with Azure Databricks

By : Alan Bernardo Palacio
Book Image

Distributed Data Systems with Azure Databricks

By: Alan Bernardo Palacio

Overview of this book

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 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.
Table of Contents (17 chapters)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Azure Resource Manager templates

ARM templates are infrastructure as code and allow us to deploy resources automatically in an agile manner. These templates are JSON files that define infrastructure and configuration in a declarative way, specifying resources and properties. We can deploy several resources as a single resource, and modify existing configurations. Just like code, it can be stored in a repository and versioned, and anyone can run the code to deploy similar environments.

ARM templates are then passed to the ARM API, which deploys the specified resources. These can include virtual networks, VMs, or an Azure Databricks workspace.

These templates have two modes of operation, which are Complete or Incremental mode. When we deploy in Complete mode, this deletes any objects that are not specified in the template and the resource group that is being deployed to. Incremental deployment adds additional resources to the existing ones.

The limitation of these templates...