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)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Chapter 3: Creating ETL Operations with Azure Databricks

In this chapter, we will learn how to set up different connections to use external sources of data such as Simple Storage Service (S3), set up our Azure Storage account, and use Azure Databricks notebooks to create extract, transform, and load (ETL) operations that clean and transform data. We will leverage Azure Data Factory (ADF), and finally, we will look at an example of designing an ETL operation that is event-driven. By exploring the sections in this chapter, you will be able to have a high-level understanding of how data can be loaded from external sources and then transformed into data pipelines, constructed and orchestrated using Azure Databricks. Let's start with a brief overview of Azure Data Lake Storage Gen2 (ADLS Gen2) and how to use it in Azure Databricks.

In this chapter, we will look into the following topics:

  • Using ADLS Gen2
  • Using S3 with Azure Databricks
  • Using Azure Blob storage with...