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


The development process of machine learning models can be a complicated task because of the inherent mixed background of the discipline and the fact that it is commonly detached from the common software development lifecycle. Moreover, we will encounter issues when transitioning the models from development to production if we are not able to export the used preprocessing pipeline that was used to extract features of the data.

As we have seen in this chapter, we can tackle issues using MLflow to manage the model lifecycle and apply staging and version control to the models used, and effectively serialize the preprocessing pipeline to be used to preprocess data to be inferred.

In the next chapter, we will explore the concept of distributed learning, a technique in which we can distribute the training process of deep learning models to many workers effectively in Azure Databricks.