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

Summary

Implementing a data lake is a paradigm change within an organization. Delta Lake provides a solution for this when we are dealing with streams of data from different sources, when the schema of the data might change over time, and when we need to have a system that is reliable against data mishandling and easy to audit.

Delta Lake fills the gap between the functionality of a data warehouse and the benefits of a data lake while also overcoming most of its challenges.

Schema validation ensures that our ETL pipelines maintain reliability against changes in the tables. It informs us of this by raising an exception if any mismatches arise and the data becomes contaminated. If the change was intentional, we can use schema evolution.

Time travel allows us to access historic versions of data, thanks to its ordered transaction log. This keeps track of every operation that's performed in Delta tables. This is useful when we need to define pipelines that need to query different...