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

Transforming and cleaning data

After our data has been loaded into a Spark dataframe, we can manipulate it in different ways. We can directly manipulate our Spark dataframe or save the data to a table, and use Structured Query Language (SQL) statements to perform queries, data definition language (DDL), data manipulation language (DML), and more.

You will need to have the Voting_Turnout_US_2020 dataset loaded into a Spark dataframe.

Spark data frames

A Spark data frame is a tabular collection of data organized in rows with named columns, which in turn have their own data types. All this information is stored as metadata that we can access using displaySchema in order to display the data types of each column or display the actual data, or describe in order to view the statistical summary of the data. One of its characteristics is that it is able to handle big amounts of data thanks to its distributed nature.

We can perform transformations such as selecting rows and columns...