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

Chapter 2: Creating an Azure Databricks Workspace

In this chapter, we will apply all the concepts we explored in Chapter 1, Introduction to Azure Databricks. We will create our first Azure Databricks workspace using the UI, and then explore the different possibilities of resource management through the Azure CLI, how to deploy these resources using the ARM template, and how we can integrate Azure Databricks within our virtual network using VNet injection.

In this chapter, we will discuss the following topics:

  • Using the Azure portal UI
  • Examining Azure Databricks authentication
  • Working with VNets in Azure Databricks
  • Azure Resource Manager templates
  • Setting up the Azure Databricks CLI

We will first begin by creating our workspace from the Azure portal UI.