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

Using the Structured Streaming API

Structured Streaming is integrated into the PySpark API and embedded in the Spark DataFrame API. It provides ease of use when working with streaming data and, in most cases, it requires very small changes to migrate from a computation on static data to a streaming computation. It provides features to perform windowed aggregation and for setting the parameters of the execution model.

As we have discussed in previous chapters, in Azure Databricks, streams of data are represented as Spark dataframes. We can verify that the data frame is a stream of data by checking that the isStreaming property of the data frame is set as true. In order to operate with Structured Streaming, we can summarize the steps as read, process, and write, as exemplified here:

  1. We can read streams of data that are being dumped in, for example, an S3 bucket. The following example code shows how we can use the readStream method, specifying that we are reading a comma-separated...