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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

Streaming ingestion architectures

While batch ingestion architectures are designed to receive a collection of data at once, streaming ingestion architectures receive data in real time, as soon as a new event occurs in the streaming data sources. Examples of streaming data sources are given here:

  • IoT sensors in a manufacturing process
  • Server and security logs
  • Click-stream data from apps and websites
  • Stock values
  • Live sport updates
  • Real-time traffic updates

Having a real-time data source does not necessarily mean you need a streaming ingestion architecture to ingest the data. Data can also be buffered at the source and ingested in batches. This could be more cost-effective as streaming ingestion architectures tend to be more expensive. Streaming ingestion architectures are recommended when the volume and velocity of data are too big to handle at the source or in use cases where decisions need to be made in real time. Examples of such use cases are given...

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