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

Triggering streaming query executions

Triggers are a way in which we define events that will lead to an operation being executed on a portion of data, so they handle the timing of streaming data processing. These triggers are defined by intervals of time in which the system checks if new data has arrived. If this interval of time is too small this will lead to unnecessary use of resources, so it should always be an amount of time customized according to your specific process.

The parameters of the triggers of the streaming queries will define if this query is to be executed as a micro-batch query on a fixed batch interval or as a continuous processing query.

Different kinds of triggers

There are different kinds of triggers available in Azure Databricks that we can use to define when our streaming queries will be executed. The available options are outlined here:

  • Unspecified trigger: This is the default option and means that unless specified otherwise, the query will...