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Key benefits

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

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 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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Distributed Data Systems with Azure Databricks

£35.99

£41.99

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

£36.99

Total £ 114.97

Steef-Jan Jun 22, 2021

Azure Databricks is a first-party Microsoft Azure service that is sold and supported directly by Microsoft. The service is available since 2018 and now available in 30 regions, including the recent addition of Azure China. Currently, there are a few books available on Databricks, and this book is a more recent one.I received this book before it was released from a Packt representative. It starts with the introduction of Azure Databricks to provide the reader a solid foundation for the rest of the text. Next, it dives into setting up an Azure workspace – mandatory when following the content in the second section, including ETL Operations, Delta Lake, Delta Engine, and structured streaming. Chapters in this section go in-depth into Delta Lake - the open format storage layer that delivers reliability, security, and performance on the data lake (both streaming and batch operations). The third and final section contains chapters on Machine- and Deep Learning with Databricks – describing the usage of Python Libraries, runtimes for Machine- and deep learning, model tracking and tuning, managing and serving Models with MLflow and MLeap, and disturbed deep learning.Most of the chapters include technical requirements to allow the reader to follow the hands-on instructions for setting up the Databricks environment, the ADSL Gen2 data lake, and so on. The hands-on and accompanying text provides a good understanding of the technology – essential when working with Azure Databricks in real-world projects – and the author does an excellent job with that. Note that most of the code examples are modifications of the official libraries or were taken from the Azure Databricks documentation to provide well-documented examples.In my view, the book is an excellent starting point for those unfamiliar with Azure Databricks and who want to invest time to learn the concepts – not only by reading but also by getting their hands dirty.

Amazon Verified review

Amazon Customer Sep 02, 2021

This book gives a very detailed explanation on everything Databricks related, there are a lot of practical examples. For me, this book was a great start to learn Databricks and explore its possibilities, it is a very hands on, step by step guide on Databricks core functionalities.

Amazon Verified review

ryanmark Dec 24, 2021

The book begins with a thorough description of the background of Databricks, its heritage in Spark, and the rationale for using Databricks as a “front end” for Spark.The key concepts of Databricks are also explained clearly in the first chapter. The descriptions are clear enough to make them accessible to a non-specialist and specific enough to be useful.The second chapter leads you step-by-step through creating a workspace in Databricks on Azure, including thorough descriptions of how to use the UI.Chapter 3 describes how to set up ETL operations, including getting data from an AWS bucket.The book is accompanied with a github repo that includes detailed code examples to go along with many chapters in the book.I can only see one drawback of the book – the absence of any mention of Google Cloud Platform’s support for Databricks. I understand that the book is focused on Databricks on Azure, but in a number of places the book mentions using data that’s resident on AWS, so it’s clear the book is not entirely Azure-centric. To address the GCP gap in the current book, I encourage the author to consider creating an update of the book that focuses on DataBricks on Google Cloud.

Amazon Verified review

CJW Jun 15, 2021

Azure is increasingly popular among the companies I know; both for hosting and ML capabilities. Further, Azure ML is apparently offering some great functionalities on the #MLOps front but sometimes you need big guns for #DataOps too. That’s why my business partner and I were curious to learn more about Azure Databricks.To do so, we picked the book Distributed Data Systems with Azure Databricks by Alan Bernardo Palacio.It’s impressive to read about the powerful data pipelines functionalities. But we certainly got the impression that — as with any other powerful tool — it is crucial to master and use it well. The same goes to the MLOps side, which Azure Databricks is now covering as well.My colleague and I haven’t had much hands-on experience with Databricks, but this book covered what’s possible and left us with a sense of how much work it could be to set things up correctly, and use the solution efficiently. Not exactly ‘two clicks away’, but certainly manageable. Exactly what we needed to learn.It’s a good book to skim for a general ‘what’s this Databricks?’ overview and then keep scanning for specific ‘how to?’ sections. - CJW & AV

Amazon Verified review

Björn Peters Aug 30, 2021

dieses Buch ist KEINE Einführung in Azure Databricks, sondern setzt gewisse Kenntnisse voraus, was man auch in zahlreichen Kapitel feststellt, denn der allgemeine Umgang mit einzelnen Tools wie Python oder dem Azure Portal werden vorausgesetzt, d.h. man sollte schon längere Zeit damit gearbeitet haben um das Buch im vollen Umfang zu nutzen. Es wird nicht mehr im Detail darauf eingegangen, wie man Jupyter Notebooks erstellt oder gewisse Services miteinander verbindet.Für mich persönlich werden zwar alle Aspekte rund um Azure Databricks und die "umgebenden" Systeme wie zum Beispiel den Azure Data Lake angesprochen, aber in manchen Punkten/Kapiteln zu viel weggelassen bzw vorausgesetzt. ABER das Buch zeigt die einzelnen Schritte auf, wie man zu einer vollumgfänglichen Lösung mit Databricks kommen kann.Inhalt gut strukturiert und verständlich/nachvollziehbar aufgebaut, man kann damit arbeiten. Ob ich mir das Buch noch einmal kaufen würde...

Amazon Verified review

Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines

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