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)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Time-series data sources

In data science and engineering, one of the most common challenges is temporal data manipulation. Datasets that hold geospatial or transactional data, which mostly lie in the financial and economics area of an application, are some of the most common examples of data that is indexed by a timestamp. Working in areas such as finance, fraud, or even socio-economic temporal data ultimately leads to the need to join, aggregate, and visualize data points.

This temporal data regularly comes in datetime formats that might vary not only in the format itself but in the information that it holds. One of the examples of this is the difference between the DD/MM/YYYY and MM/DD/YYYY format. Misunderstanding these different datetime formats could lead to failures or wrongly formed results if the formats used don't match up. Moreover, this data doesn't come in numerical format, which—as we have seen in previous sections of the chapter—can lead to...