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

Installing libraries in Azure Databricks

We can make use of third-party or custom code by installing libraries written in Python, Java, Scala, or R. These libraries will be available to notebooks and jobs running on your clusters depending on the level at which the libraries were installed.

In Azure Databricks, installing libraries can be done in different ways, the most important decision being at which level we will be installing these libraries. The options available are at the workspace, cluster, or notebook level:

  • Workspace libraries serve as a local repository from which you create cluster-installed libraries. A workspace library might be custom code created by your organization or might be a particular version of an open-source library that your organization has standardized on.
  • Cluster libraries are available to be used by all notebooks attached to that cluster. You can install a cluster library from a public repository or create one from a previously installed...