What this book covers
Chapter 1, Overview of Microsoft Fabric and Understanding Its Different Concepts, introduces the overall analytics landscape of Microsoft Fabric and other Microsoft offerings, and it discusses how Fabric has leaped ahead of other products/platforms available on the market with its unique differentiators.
Chapter 2, Understanding Different Workloads and Getting Started with Microsoft Fabric, sets the stage for the various workloads that are built into Microsoft Fabric, including Data Factory, Data Engineering, Data Science, Data Warehouse, Real-Time Analytics, as well as Power BI. This chapter helps you to understand each workload and correlate them to your skillsets and environment, ensuring that you understand the full environment when each workload is discussed in later chapters.
Chapter 3, Building an End-to-End Analytics System – Lakehouse, walks you through building an end-to-end lakehouse solution. This chapter works in parallel to the following data warehouse chapter, by showing you how a data engineer professional can be successful in Microsoft Fabric by leveraging Spark skillsets.
Chapter 4, Building an End-to-End Analytics System – Data Warehouse, takes you through building an end-to-end data warehouse solution. This chapter works in parallel to the previous lakehouse chapter by showing how a data warehouse professional can be successful in Microsoft Fabric.
Chapter 5, Building an End-to-End Analytics System – Real-Time Analytics, explores Fabric’s real-time analytics by creating a KQL database, connecting to an eventstream that simulates data, and analyzing it using KQL. You will understand the types of sources that can be consumed, see how data is sent to OneLake for consumption by other compute engines, and be introduced to KQL.
Chapter 6, Building an End-to-End Analytics System – Data Science, shows you how to implement and operationalize machine learning and artificial intelligent models end to end, all the way from data ingesting to cleansing, preparing, visualizing for exploratory analysis, tracking, and training the model. Further, it will cover the process to operationalize the model for scoring (batch and interactive).
Chapter 7, Monitoring Overview and Monitoring Different Workloads, covers monitoring different aspects of Fabric using Monitoring hub, including Data Factory pipelines, Spark jobs for both the data science and data engineering workloads, using DMVs and query insights to monitor the data warehouse, and the KQL database activity for real-time analytics. Additionally, it covers the capacity metrics app that can be used to gain insights into the capacity unit usage.
Chapter 8, Administering Fabric, examines how to enable Fabric for a tenant, the options to overwrite the tenant-level settings at a capacity level, and how to associate a capacity with a workspace. We will also cover the different types of capacities and how tenants, capacities, and workspaces are all tied together. This information is vital to being able to administer Fabric effectively across teams and capacities.
Chapter 9, Security and Governance Overview, explores tenant-level Fabric security settings, how Entra ID is used to allow external users access to Fabric, and how to secure workspaces and items. It also demonstrates how to use Purview for data cataloging and governance, and domains for data organization.
Chapter 10, Continuous Integration and Continuous Deployment (CI/CD), covers DevOps process and teaches how to implement CI/CD to move your code items from one environment (development) to another (test or production).
Chapter 11, Overview of AI Assistance and Copilot Integration, explores how AI is used to extend the developer experience and provide deeper insights into your data with the Copilot experiences built into each workload. This includes an overview of the Copilots for data science, data engineering, Data Factory, and Power BI, as well as the tenant-level settings required to enable the capabilities in the Fabric tenant.
