Book Image

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
Book Image

Azure Data and AI Architect Handbook

By: Olivier Mertens, Breght Van Baelen

Overview of this book

With data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.
Table of Contents (18 chapters)
1
Part 1: Introduction to Azure Data Architect
4
Part 2: Data Engineering on Azure
8
Part 3: Data Warehousing and Analytics
13
Part 4: Data Security, Governance, and Compliance

AI architectures on Azure

To understand the addition of AI components in a larger solution, we will take a look at some common architectures. Similar to the way data is ingested, machine learning predictions occur either in real time or in batches.

Let’s explore a sample architecture of each approach.

Scoring data in batches

The following figure is an example of a data architecture on Azure with batch scoring.

Figure 9.10 – An example data architecture involving batch scoring in the ETL process

Figure 9.10 – An example data architecture involving batch scoring in the ETL process

Using data from the data lake and data warehouse, a custom model can be developed in the Azure Machine Learning workspace. The workspace does not copy data, so there are no additional storage costs. It will either mount data from the data lake or load tabular data straight into memory during training jobs in machine learning pipelines. When performing batch scoring, we do not want to deploy the model as an endpoint but, rather, have a deployed...