Chapter 1, Planning Power BI Projects, discusses alternative deployment modes for Power BI, team and project roles, and licensing. Additionally, an example project template and its corresponding planning and dataset design processes are described.
Chapter 2, Connecting to Sources and Transforming Data with M, depicts the data access layer supporting a Power BI dataset, including data sources and fact and dimension table queries. Concepts of the Power Query M language, such as query folding and parameters, are explained and examples of custom M queries involving conditional and dynamic logic are given.
Chapter 3, Designing Import and DirectQuery Data Models, reviews the components of the data model layer and design techniques in support of usability, performance, and other objectives. These topics include relationship cross-filtering, custom sort orders, hierarchies, and metadata.
Chapter 4, Developing DAX Measures and Security Roles, covers the implementation of analysis expressions reflecting business definitions and common analysis requirements. Primary DAX functions, concepts, and use cases such as date intelligence, row-level security roles, and performance testing are examined.
Chapter 5, Creating and Formatting Power BI Reports, describes a report planning process, data visualization practices, and report design fundamentals, including visual selection and filter scopes. Top report development features, such as slicer visuals, tool tips, and conditional formatting are also reviewed.
Chapter 6, Applying Custom Visuals, Animation, and Analytics, examines powerful interactive and analytical features, including drillthrough report pages, bookmarks, the Analytics pane, ArcGIS Maps, and the waterfall charts. Additionally, examples of custom visuals, such as the Power KPI, and the capabilities of animation to support data storytelling are provided.
Chapter 7, Designing Power BI Dashboards and Architectures, provides guidance on visual selection, layout, and supporting tiles to drive effective dashboards. Alternative multi-dashboard architectures, such as an organizational dashboard architecture, are reviewed, is the configuration of dashboard tiles and mobile optimized dashboards.
Chapter 8, Managing Application Workspaces and Content, features the role and administration of app workspaces in the context of Power BI solutions and staged deployments. Additionally, the Power BI REST API, content management features, and practices are reviewed, including field descriptions and version history.
Chapter 9, Managing the On-Premises Data Gateway, covers top gateway planning considerations, including alternative gateway architectures, workloads, and hardware requirements. Gateway administration processes and tools are described, such as the manage gateways portal, gateway log files, and PowerShell Gateway commands.
Chapter 10, Deploying the Power BI Report Server, contrasts the Power BI Report Server with the Power BI cloud service and provides guidance on deployment topics such as licensing, reference topology, configuration, administration, and upgrade cycles.
Chapter 11, Creating Power BI Apps and Content Distribution, walks through the process of publishing and updating apps for groups of users. Additionally, other common distribution methods are covered, such as the sharing of reports and dashboards, email subscriptions, data-alert-driven emails, and embedding Power BI content in SharePoint Online.
Chapter 12, Administering Power BI for an Organization, highlights data governance for self-service and corporate BI, Azure Active Directory features such as Conditional Access Policies, and the Power BI admin portal. Details are provided about configuring Power BI service tenant settings, managing Power BI Premium capacities, and the tools available to monitor Power BI activities.
Chapter 13, Scaling with Premium and Analysis Services, reviews the capabilities of Power BI Premium and alternative methods for allocating premium capacity. Additionally, Power BI datasets are contrasted with Analysis Services models, Azure Analysis Services is contrasted with SQL Server Analysis Services, and the migration of a Power BI dataset to an Analysis Services model is described.