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

Chapter 6: Introducing Structured Streaming

Many organizations have a need to consume large amounts of data continuously in their everyday processes. Therefore, in order to be able to extract insights and use the data, we need to be able to process this information as it arrives, resulting in a need for continuous data ingestion processes. These continuous applications create a need to overcome challenges such as creating a reliable process that ensures the correctness of the data, despite possible failures such as traffic spikes, data not arriving in time, upstream failures, and so on, which are common when working with continuously incoming data or transforming data without consistent file formats that have different structure levels or need to be aggregated before being used.

The most traditional way of dealing with these issues was to work with batches of data executed in periodic tasks, which processed raw streams and data and stored them into more efficient formats to allow...