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

Using Azure Blob storage with Azure Databricks

In the same way that we can access objects stored in AWS S3, we can access objects in Azure Blob storage. Both options allow us to have a redundant data storage that can be accessed from anywhere. Their differences lie in the tools that they will be used with and certain characteristics that may make them more suitable to use in a certain project. Azure Blob storage is more cost-efficient and has high redundancy, while S3 is extensively used by several organizations and has a small learning curve. We will see how to set up an Azure Blob storage account, upload the file that we were using, and read it from our notebook.

Setting up Azure Blob storage

The first step is to create an Azure Blob storage account, as follows:

  1. Search for Storage account in the Azure portal and select Create a new storage account.
  2. In the Create storage account options, fill out the details and set the Replication option to Locally-redundant storage...