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

PySpark API

We have been using the PySpark API across all sections when describing the features of Azure Databricks without discussing too much of its functionalities and how we can leverage them to make reliable ETL operations when working with big data. PySpark is the Python API for Apache Spark, a cluster-computing framework that is the heart of Azure Databricks.

Main functionalities of PySpark

PySpark allows you to harness the power of distributed computing with the ease of use of Python and it's the default way in which we express our computations through this book unless stated otherwise.

The fundamentals of PySpark lies in the functionality of its sub-packages of which the most central are the following:

  • PySpark DataFrames: Data stored in rows following a set of named columns. These DataFrames are immutable and allow us to perform lazy computations.
  • The PySpark SQL module: A higher-abstraction module for processing structured and semi-structured datasets...