Distributed Data Systems with Azure Databricks

Distributed Data Systems with Azure Databricks
  • eBook:
    Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines
  • Author:
    Alan Bernardo Palacio
  • Edition:
  • Categories:
  • Data:
    June 9, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    372 pages
  • Format:
    PDF, ePUB

Book Description
Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks

Key Features

  • Get to grips with the distributed training and deployment of machine learning and deep learning models
  • Learn how ETLs are integrated with Azure Data Factory and Delta Lake
  • Explore deep learning and machine learning models in a distributed computing infrastructure

Book Description

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 inference using TensorFlow and TensorRT with the ResNet-50 model. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement Data Definition Language (DDL) 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.

What you will learn

  • Create ETLs for big data in Azure Databricks
  • Train, manage, and deploy machine learning and deep learning models
  • Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation
  • Discover how to use Horovod for distributed deep learning
  • Find out how to use Delta Engine to query and process data from Delta Lake
  • Understand how to use Data Factory in combination with Databricks
  • Use Structured Streaming in a production-like environment

Who This Book Is For

This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.


Section 1: Introducing Databricks
1. Introduction to Azure Databricks
2. Creating an Azure Databricks Workspace

Section 2: Data Pipelines with Databricks
3. Creating ETL Operations with Azure Databricks
4. Delta Lake with Azure Databricks
5. Introducing Delta Engine
6. Introducing Structured Streaming

Section 3: Machine and Deep Learning with Databricks
7. Using Python Libraries in Azure Databricks
8. Databricks Runtime for Machine Learning
9. Databricks Runtime for Deep Learning
10. Model Tracking and Tuning in Azure Databricks
11. Managing and Serving Models with MLflow and MLeap
12. Distributed Deep Learning in Azure Databricks

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