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

Setting up the Azure Databricks CLI

Azure Databricks comes with a CLI tool that allows us to manage our resources. It's built on top of the Azure Databricks API and allows you to access the workspace, jobs, clusters, libraries, and more. This is an open source project hosted on GitHub.

The Azure Databricks CLI is based on Python 3 and is installed through the following pip command:

pip3 install databricks-cli

You can confirm that the installation was successful by checking the version. If the installation was successful, you will see as a result the current version of the Azure Databricks CLI:

databricks --version

It's good to bear in mind that using the Databricks CLI with firewall-enabled storage containers is not possible and, in that case, it is recommended to use Databricks Connect or AZ storage.

To be able to install the Azure CLI, you will need to have Python 3 already installed and added to the path of the environment you will be working on.