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

Optimizing join performance

Performing joins on tables can be a resource-expensive operation. To improve the performance of such operations, we can select a subset of the data or correct possible drawbacks, such as having a disproportionate distribution of file sizes in our data. Solving these issues can improve performance and lead to more efficient use of distributed computing power.

Azure Databricks Delta Lake allows optimization of join operations by providing range filtering and correcting skewness in the distribution of the file size of the data in our tables.

Range join optimization

Joins are used frequently, so optimizing these operations can lead to a great improvement in the performance of our queries. Range join optimization is the process of specifying that a join needs to be performed on a subset of data given by a range.

Range join optimization is applied when join operations have a filtering condition whose type is either a numeric or datetime type, and can...