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

Streaming table read and writes

Although we have already mentioned that structured streaming is a core part of the Apache Spark API, in this section, we will dive into how we can use it as a reliable stream processing engine, in which computation can be performed in the same way as batch computation is performed on static data. Along with Auto Loader, it will automatically handle data being streamed into Delta tables without the common inconveniences such as merging small files produced by low latency ingestion, running concurrent batch jobs when working with several streams, and, as we discussed earlier, keeping track of the files available for being streamed into tables.

Let's learn how to stream data into Delta tables in Azure Databricks.

Streaming from Delta tables

You can use a Delta table as a stream source for streaming queries. The query will then process any existing data in the table and any incoming data from the stream. Let's take a look:

  • We can...