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)
Section 1: Introducing Databricks
Section 2: Data Pipelines with Databricks
Section 3: Machine and Deep Learning with Databricks


In this chapter, we explored the possibilities of creating an Azure Databricks service either through the UI or the ARM templates, and explored the options in terms of enforcing access control on resources. We also reviewed the different authentication methods, the use of VNets to have a consistent approach when dealing with access policies throughout Azure resources, and how we use the Databricks CLI to create and manage clusters, jobs, and other assets. This knowledge will allow us to efficiently deploy the required resources to work with data in Azure Databricks while maintaining control over how these assets access and transform data.

In the next chapter, we will apply this and the previous concepts to run more advanced notebooks, create ETLs, data science experiments, and more. We'll start with ETL pipelines in Chapter 3, Creating ETL Operations with Azure Databricks.