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

Orchestrating jobs with Azure Databricks

Until now, we have been able to use data stored in either an S3 bucket or Azure Blob storage, transform it using PySpark or SQL, and then persist the transformed data into a table. Now, the question is: Which methods do we have to integrate this into a complete ETL? One of the options that we have is to use ADF to integrate our Azure Databricks notebook as one of the steps in our data architecture.

In the next example, we will use ADF in order to trigger our notebook by directly passing the name of the file that contains the data we want to process and use this to update our voting turnout table. For this, you will require the following:

  • An Azure subscription
  • An Azure Databricks notebook attached to a running container
  • The Voting_Turnout_US_2020 dataset loaded into a Spark dataframe

ADF

ADF is the Azure cloud platform for the integration of serverless data transformation and aggregation processes. It can integrate...