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

Chapter 5: Introducing Delta Engine

Delta Engine is the query engine of Delta Lake, which is included by default in Azure Databricks. It is built in a way that allows us to optimize the processing of data in our Delta Lake in a variety of ways, thanks to optimized layouts and improved data indexing. These optimization operations include the use of dynamic file pruning (DFP), Z-Ordering, Auto Compaction, ad hoc processing, and more. The added benefit of these optimization operations is that several of these operations take place in an automatic manner, just by using Delta Lake. You will be using Delta Engine optimization in many ways.

In this chapter, you will learn how to make use of Delta Lake to optimize your Delta Lake ETL in Azure Databricks. Here are the topics on which we will center our discussion:

  • Optimizing file management with Delta Engine
  • Optimizing queries using DFP
  • Using Bloom filters
  • Optimizing join performance

Delta Engine is all about optimization...