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

Batch ingestion architectures

The simplest form of ingestion architecture is a use case where data is only ingested in batches from other cloud-based sources (no sources residing on-premises). In this case, we will use data pipelines to periodically fetch large amounts of data and write them to the bronze layer in the data lake. Note that we restrain from performing any kind of transformation in this initial pipeline.

We will look at ingesting data from the following sources:

  • Cloud sources
  • On-premises sources

Let’s first look at how to ingest data from cloud-based sources.

Ingesting data from cloud sources

When ingesting data from other cloud sources, the connection is often more convenient, Also, we can make use of Azure-hosted integration runtimes (IRs). This will serve as the compute for the pipeline orchestration in either Azure Data Factory or Azure Synapse pipelines. Other Data Factory components will be more elaborately discussed in the next...