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Distributed Data Systems with Azure Databricks
Distributed Data Systems with Azure Databricks

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

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Distributed Data Systems with Azure Databricks

Chapter 1: Introduction to Azure Databricks

Modern information systems work with massive amounts of data, with a constant flow that increases every day at an exponential rate. This flow comes from different sources, including sales information, transactional data, social media, and more. Organizations have to work with this information in processes that include transformation and aggregation to develop applications that seek to extract value from this data.

Apache Spark was developed to process this massive amount of data. Azure Databricks is built on top of Apache Spark, abstracting most of the complexities of implementing it, and with all the benefits that come with integration with other Azure services. This book aims to provide an introduction to Azure Databricks and explore the applications it has in modern data pipelines to transform, visualize, and extract insights from large amounts of data in a distributed computation environment.

In this introductory chapter, we will explore these topics:

  • Introducing Apache Spark
  • Introducing Azure Databricks
  • Discovering core concepts and terminology
  • Interacting with the Azure Databricks workspace
  • Using Azure Databricks notebooks
  • Exploring data management
  • Exploring computation management
  • Exploring authentication and authorization

These concepts will help us to later understand all of the aspects of the execution of our jobs in Azure Databricks and to move easily between all its assets.

Technical requirements

To understand the topics presented in this book, you must be familiar with data science and data engineering terms, and have a good understanding of Python, which is the main programming language used in this book, although we will also use SQL to make queries on views and tables.

In terms of the resources required, to execute the steps in this section and those presented in this book, you will require an Azure account as well as an active subscription. Bear in mind that this is a service that is paid, so you will have to introduce your credit card details to create an account. When you create a new account, you will receive a certain amount of free credit, but there are certain options that are limited to premium users. Always remember to stop all the services if you are not using them.

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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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 25, 2021
Length: 414 pages
Edition : 1st
Language : English
ISBN-13 : 9781838647216
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Microsoft
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Length: 414 pages
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Frequently bought together


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Total S$ 205.97
Distributed Data Systems with Azure Databricks
S$63.99
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Total S$ 205.97 Stars icon

Table of Contents

16 Chapters
Section 1: Introducing Databricks Chevron down icon Chevron up icon
Chapter 1: Introduction to Azure Databricks Chevron down icon Chevron up icon
Chapter 2: Creating an Azure Databricks Workspace Chevron down icon Chevron up icon
Section 2: Data Pipelines with Databricks Chevron down icon Chevron up icon
Chapter 3: Creating ETL Operations with Azure Databricks Chevron down icon Chevron up icon
Chapter 4: Delta Lake with Azure Databricks Chevron down icon Chevron up icon
Chapter 5: Introducing Delta Engine Chevron down icon Chevron up icon
Chapter 6: Introducing Structured Streaming Chevron down icon Chevron up icon
Section 3: Machine and Deep Learning with Databricks Chevron down icon Chevron up icon
Chapter 7: Using Python Libraries in Azure Databricks Chevron down icon Chevron up icon
Chapter 8: Databricks Runtime for Machine Learning Chevron down icon Chevron up icon
Chapter 9: Databricks Runtime for Deep Learning Chevron down icon Chevron up icon
Chapter 10: Model Tracking and Tuning in Azure Databricks Chevron down icon Chevron up icon
Chapter 11: Managing and Serving Models with MLflow and MLeap Chevron down icon Chevron up icon
Chapter 12: Distributed Deep Learning in Azure Databricks Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(8 Ratings)
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4 star 37.5%
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2 star 12.5%
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Steef-Jan Jun 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Customer Sep 02, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
CJW Jun 15, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
ryanmark Dec 24, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Björn Peters Aug 30, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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 Amazon
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