Rohan Desai Aug 16, 2023

The book "Azure Data and AI architecture Handbook" by Oliver and Breght is an insightful guide that skillfully navigates the intricate intersection of data architecture and artificial intelligence. It offers a comprehensive exploration of the vital role data architecture plays in the realm of AI, making it a must-read for both beginners and seasoned professionals in the field.The book delves into the foundational concepts of data architecture, gradually intertwining them with cutting-edge AI principles. The author's ability to explain complex concepts in an accessible manner is commendable, making it a suitable resource for readers with varying levels of expertise. From discussing data modeling techniques to elucidating the intricacies of neural networks and machine learning algorithms, the book covers a wide spectrum of topics.This book starts with a glimpse of data architecture and preparation for cloud adoption. Further, this book focuses more on data massaging concepts with Azures' perspective.This book provides an in-depth process of transforming data using multiple methods like sql scripts, ssis, and spark notebooks, with a detailed outline of batch and streaming ingestion.It also explains data warehousing concepts from scratch with an explanation of SCD concepts and its implementation.This book also covers the data security, governance, and compliance topics that would enlighten you on enterprise level data governance, its importance, data protection, access control, and threat protection.At the academic or professional level, this book servers a good source of learning for someone who would start their Azure journey as a data modeler or data engineer.

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Tanya Silva Aug 08, 2023

I have received this book for review purposes from the publisher.Review ( 8/8/23): I have spent the last few weeks reading Azure Data and AI Architect Handbook, wishing something like this existed many years ago when I was starting in the field of AI/ML, Data Engineering, Data Services, Data Security, Data Science, Data Governance, and other topics which with the rise of the interest in GenAI are coming into the light as well. I highly recommend this book to a new college graduate just starting their career in this fascinating space as well as to a seasoned professional who might have worked with Microsoft offerings for many years and would like to understand the latest technology development.The book covers Introduction to Data Architecture, Data Engineering, Data Warehousing and Analytics, and Data Security, Governance, and Compliance.In the Data Architecture section, topics such as reference diagrams with the various layers are covered, as well as data orchestration, processing, security, monitoring, etc. Once the reader understands the data architecture, the authors provide insight into the challenges with on-prem solutions and what is needed to prepare for Cloud Adoption.The Data Engineering section covers data ingestion, transformations, and storage. Each topic in itself is a vast area requiring deep subject matter expertise. However, for an AI architect, I suggest having at least a high-level understanding of how this area is done, what the challenges are, what the bottlenecks are, and what architectural decisions might be made at this step that might impact AI products down the line.The Data Warehousing and Analytics section covers DW ( the speed of your visualizations/downstream apps will be extremely dependent on this step). The semantic layer ( okay, I admit, I am biased when I have to do data analysis. I like it in a multidimensional format, but over the years, I have found it is difficult for humans to grasp high-level dimensionality). Power BI - even if you do not use Power BI, I highly recommend installing a free desktop version to play with it - you might be pleasantly surprised. The last topic in this section covers Advanced Analytics using AI - Azure Cognitive Services, Azure OpenAI, LLMs, and such. Depending on when you are reading this review, the digital version of the book may be modified to include new developments in this space.The last part covers Data Security, Governance, and Compliance - this topic sometimes gets on the backburner when the solutions are being architectured, but I suggest including this in the first rounds of your product development roadmap. I forecast a lot of development in legal, security, and privacy space coming up in the AI field.Overall, I highly recommend AI Architects have this book in their library as a reference so that they can envision end-to-end solutions on Azure and be aware of all pieces of the puzzle.

Amazon Verified review

Sangita Mahala Sep 01, 2023

This book is a comprehensive guide to designing and implementing data platforms on Azure, exploring architectural design patterns, determining data storage and warehouse solutions, utilizing OpenAl and custom ML models for advanced analytics, and managing data estate security. I highly recommend this book to anyone who is interested in a career in cloud data and AI architecture.

Amazon Verified review

Advitya Gemawat Nov 06, 2023

The book covers several essential concepts and skills needed to design, implement, and manage data and AI solutions on Azure. It also provides practical guidance and best practices for various scenarios and challenges that data and AI architects may encounter.Some of the topics that I found particularly useful were:🛠 Using Azure Synapse Analytics to build a modern data warehouse that can handle both structured and unstructured data at scale. The book explains how to use Synapse SQL, Spark, and Synapse Pipelines to ingest, transform, and analyze data from various sources. It also shows how to use Synapse Studio, a unified web-based interface that simplifies the development and management of data and AI projects. Synapse's capabilities may be further augmented by utilizing integrations with other data products as part of Microsoft Fabric's platform.📈 Leveraging Azure Machine Learning (AML) to create, train, and deploy machine learning models on the cloud. The book also demonstrates how to quickly get started with AML designer, a drag-and-drop tool that allows you to build machine learning pipelines without writing code.🧰 I also love that this book includes description on several basic concepts around LLMs (such as Fine-tuning, Grounding etc), describing the use-cases of several foundational models, and building custom applications with the Azure Open AI (AOAI) Service.In my view, what especially makes this book resourceful for non-technical and technical audiences is its emphasis on explanation of basic concepts around cloud architectures and ML modeling along with code examples to get started.

Amazon Verified review

Udbhav Aug 23, 2023

As an IT professional looking to level up my Azure architect skills, this book has been an absolute game-changer. The author really knocks it out of the park with the clean, structured approach to what can be an incredibly complex field.I was able to quickly ramp up on key concepts like Azure data storage, processing, security, Machine Learning and Cognitive Services thanks to the easy-to-digest chapters. The plentiful real-world examples and sample code snippets are invaluable for cementing the lessons too.The end-to-end case studies are the crown jewel though. Seeing how an experienced pro tackles full solution architectures on Azure gave me the confidence boost I needed to apply these skills myself. Can't stress enough how crucial the hands-on focus is here.In short - if you want to go from intermediate to advanced in designing robust data and AI systems on Azure, this is your blueprint. I honestly think this may be the best Azure architecture book out there right now. Do yourself a favor and grab a copy ASAP!

Amazon Verified review

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Azure Data and AI Architect Handbook: Adopt a structured approach to designing data and AI solutions at scale on Microsoft Azure

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