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 and streaming ingestion

Regardless of the type (batch or streaming), data ingestion is located in the first layer of the data architecture, as seen in Figure 3.1:

Figure 3.1 – Reference diagram for cloud data architectures: the ingestion layer (on the left) forms the first layer of the architecture

Figure 3.1 – Reference diagram for cloud data architectures: the ingestion layer (on the left) forms the first layer of the architecture

The ingestion layer forms the front door for the solution. Here, we pull in data using data pipelines and, in enterprise-level solutions, commonly have it land in a massive-scale, unstructured storage service such as a data lake.

The type of ingestion plays a key role in the design of a cloud data architecture. Batch ingestion was, and in most cases still is, the norm for ingesting data into the cloud. A batch approach refers to the periodical ingestion or processing of (usually large) bulks of data. Streaming ingestion, as the name suggests, involves continuous streams of data.

In general, batch ingestion and processing have long been the...