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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

Ingesting Data into the Cloud

Ingesting data into the cloud is a key step in any data pipeline and can greatly impact the efficiency and scalability of your data processing and analysis. This is why it is critical for any cloud data architect to have a deep understanding of data ingestion techniques and architectures on Azure. In this chapter, we will dive into the world of data ingestion on Azure, focusing on the key concepts of batch ingestion and data streaming, as well as various ingestion architectures.

We will begin by discussing the differences between batch ingestion and data streaming, and when to use each method. We will also explore the benefits and limitations of each approach and provide examples of use cases for each method.

Next, we will explore data ingestion architectures on Azure. We will introduce Azure Data Factory and Azure Synapse pipelines for designing and implementing data pipelines on Azure. Azure Data Lake Storage (ADLS) is introduced for permanently...

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