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Azure Data and AI Architect Handbook

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
4.5 (13)
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Azure Data and AI Architect Handbook

Azure Data and AI Architect Handbook

4.5 (13)
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)
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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

Summary

In this chapter, we provided a comprehensive overview of the various methods and tools available for getting data into the cloud. The chapter started by discussing the differences between batch ingestion and streaming ingestion and when to use each method. It explained the benefits and limitations of each approach and provided examples of use cases for each method.

One of the key tools introduced in this chapter is ADLS. This is a powerful storage solution for big data and allows for efficient and flexible storage of large datasets in the cloud. The chapter explained how ADLS can store data in a variety of formats, including structured and unstructured data. We also discussed access tiers, redundancy, and data lake tiers.

We delved into architectures for both batch ingestion from cloud sources and on-premises sources. Next, we explained streaming architectures, such as lambda and kappa architectures, which are becoming increasingly popular for real-time data ingestion...

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