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

Example on Structured Streaming

In this example, we will be looking at how we can leverage knowledge we have acquired on Structured Streaming throughout the previous sections. We will simulate an incoming stream of data by using one of the example datasets in which we have small JSON files that, in real scenarios, could be the incoming stream of data that we want to process. We will use these files in order to compute metrics such as counts and windowed counts on a stream of timestamped actions. Let's take a look at the contents of the structured-streaming example dataset, as follows:

%fs ls /databricks-datasets/structured-streaming/events/

You will find that there are about 50 JSON files in the directory. You can see some of these in the following screenshot:

Figure 6.3 – The structured-streaming dataset's JSON files

We can see what one of these JSON files contains by using the fs head option, as follows:

%fs head /databricks-datasets...