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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

To recap, data pipelines in Azure are a set of tools and services that allows for the efficient movement and transformation of data. One of the concepts covered in the chapter is the difference between ETL and ELT pipelines. In this book, we will focus mostly on ETL. The chapter also covered the differences between data pipelines in ADF and data pipelines in Azure Synapse Analytics.

We described various tools and technologies available for data transformation in Azure, including mapping data flows, Spark notebooks, SQL scripts, and SSIS packages for batch processing, and Azure Stream Analytics and Azure Databricks for real-time processing.

Next, we looked at an example architecture for both batch and stream processing, providing a high-level overview of the components and technologies involved. Later parts of the architecture remain abstract for now. We introduced a holistic flowchart to map the decision-making process when choosing one of the transformation tools discussed...

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