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 have tried to cover all the main aspects of how Azure Databricks works. Some of the things we have discovered include how notebooks can be created to execute code, how we can import data to use, how to create and manage clusters, and so on. This is important because when creating ETLs and ML experiments in Azure Databricks within an organization, aside from how to code the ETL in our notebooks, we will need to know how to manage the data and computational resources required, how to share assets, and how to manage the permissions of each one of them.

In the next chapter, we will apply this knowledge to explore in more detail how to create and manage the resources needed to work with data in Azure Databricks, and learn more about custom VNets and the different alternatives that we have in order to interact with them, either through the Azure Databricks UI or the CLI tool.