Chapter 1, Introduction to Data Architectures, introduces methods of getting business value from data to solidify any long-term data strategy. You will then get an introduction to our architecture reference diagram to give a first glance at what a bare-bones data architecture may look like. You will then learn what challenges businesses can face when retaining an on-premise-only data strategy.

Chapter 2, Preparing for Cloud Adoption, explains the economic and technical benefits of using the Azure cloud and gives an introduction to Microsoft’s Well-Architected Framework (WAF), which is used by all data and AI architects at Microsoft to guarantee high quality in the design of any data platform. Finally, you will learn how to set up a data and AI landing zone to start your journey to the Azure cloud.

Chapter 3, Ingesting Data into the Cloud, discusses the different ways of ingesting data in Azure and various reference architectures (e.g., Lambda, Kappa, and so on) to best match any requirements. You will learn how to land any streaming or batch data in scalable Azure data lakes according to best practices.

Chapter 4, Transforming Data on Azure, covers data pipelines – the key components to move data between on-premises and Azure and between various Azure components. Pipelines can move data based on a specific event, on a schedule, or in near real time, also called a streaming pipeline. You will learn about the various techniques for automating such data pipelines utilizing orchestration of the pipelines as jobs. You will also learn how to handle both batch and streaming data when orchestrating data transformations in Azure.

Chapter 5, Storing Data for Consumption, looks at best practices for early data orchestration and storage design. You will also learn about the different types of data, the requirements for different data serving methods, and the Azure resources that can be used to meet the functional and technical storage requirements for a data platform.

Chapter 6, Data Warehousing, covers the different ways of creating data warehouses in Azure, where every warehouse comes with its own pros and cons. You will learn what metrics are taken into account when choosing the right warehousing option.

Chapter 7, The Semantic Layer, explains how to implement a semantic layer in a data warehouse to improve the ease of use for end/business users. The semantic layer will hide many of the underlying complexities occurring in earlier stages of the data processing, allowing a wider audience to perform queries against the data warehouse.

Chapter 8, Visualizing Data Using Power BI, explains the options for designing enterprise dashboards and reports to render KPIs. You will learn various ways of integrating Power BI with other components of the data platform to allow for fast and easy visualization of key data.

Chapter 9, Advanced Analytics Using AI, looks at how to leverage the Azure AI services to analyze or transform data or generate new data. You will learn key questions to ask yourself to set up a solid AI strategy and get an in-depth view of the Azure OpenAI service, Azure Cognitive Services, and the Azure Machine Learning workspace, along with knowledge of the entire MLOps process.

Chapter 10, Enterprise-Level Data Governance and Compliance, covers data governance, which has quickly become a key component of every cloud data platform at scale. You will learn about core concepts within the world of data governance and how Microsoft Purview addresses many of the needs in this area. Furthermore, you will learn about data governance frameworks to help get you started on your governance journey.

Chapter 11, Introduction to Data Security, looks at how Azure was designed with security in mind. You will learn about the different layers of data security, along with some core Microsoft and Azure services to make security and monitoring airtight.