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

Structured Streaming model

A Structured Streaming model is based on a simple but powerful premise: any query executed on the data will yield the same results as a batch job at a given time. This model ensures consistency and reliability by processing data as it arrives in the data lake, within the engine, and when working with external systems.

As seen before in the previous chapters, to use Structured Streaming we just need to use Spark dataframes and the API, stating which are the I/O locations.

Structured Streaming models work by treating all new data that arrives as a new row appended to an unbound table, thereby giving us the opportunity to run batch jobs on data as if all the input were being retained, without having to do so. We then can query the streaming data as a static table and output the result to a data sink.

Structured Streaming is able to do this, thanks to a feature called Incrementalization, which plans a streaming execution every time that we run a query...