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

Using Bloom filters

Bloom filters are a way of efficiently filtering records in a database based on a condition. They have a probabilistic nature and are used to test the membership of an element in a set. We can encounter false positives but not false negatives. These filters were developed as a mathematical construct, to be applied when the amount of data to scan is impractical to be read, and are based on hashing techniques.

Delta Lake provides us with the ability to apply Bloom filters on our queries to further improve performance. We will see how they work at a basic level and how they can be applied in Delta Lake.

Understanding Bloom filters

As mentioned in the introduction to this section, Bloom filters are probabilistic data structures used to test if an element belongs to a category or not. This structure is a fixed-length bit array that is populated using a hash function, which maps the information into ones and zeros. The length of the array depends on the number...