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Supercharge performance analytics and create repeatable patterns to ensure you get the best performance and scalability from your analytics solutions with Power BIKey FeaturesLearn how to build performant data models and apply Row-Level SecurityIdentify and fix performance issues in reports, DAX, and datasets using DAX Studio/VertiPaq AnalyzerUse a formal process to manage performance, from setting targets to monitoring and remediating issuesBook DescriptionThis book comprehensively covers every layer of Power BI, from the report canvas to data modeling, transformations, storage, and architecture.Developers and architects working with any area of Power BI will be able to put their knowledge to work with this practical guide to design and implement at every stage of the analytics solution development process. This book is not only a unique collection of best practices and tips, but also provides you with a hands-on approach to identifying and fixing common performance issues.Complete with explanations of essential concepts and practical examples, you'll learn about common design choices that affect performance and consume more resources and how to avoid these problems. You'll grasp the general architectural issues and settings that broadly affect most solutions. As you progress, you'll walk through each layer of a typical Power BI solution, learning how to ensure your designs can handle scale while not sacrificing usability. You'll focus on the data layer and then work your way up to report design. We will also cover Power BI Premium and load testing.By the end of this Power BI book, you'll be able to confidently maintain well-performing Power BI solutions with reduced effort and know how to use freely available tools and a systematic process to monitor and diagnose performance problems.What you will learnUnderstand how to set realistic performance targets and address performance proactivelyUnderstand how architectural options and configuration affect performanceBuild efficient Power BI reports and data transformationsExplore best practices for data modeling, DAX, and large datasetsUnderstand the inner workings of Power BI PremiumExplore options for extreme scale with Azure servicesUnderstand how to use tools that help identify and fix performance issuesWho this book is forData analysts, BI developers, and data professionals who have learnt the basics of Power BI and now want to understand how to build advanced analytics solutions will find this business intelligence book useful. Familiarity with the major components of Power BI and a beginner-level understanding of their purpose and use cases are required. Cover Title page Copyright and Credits Dedications Foreword Contributors Table of Contents Preface Section 1: Architecture, Bottlenecks, and Performance Targets Chapter 1: Setting Targets and Identifying Problem Areas Defining good performance Report performance goals Setting realistic performance targets Considering areas that can slow you down Connecting data sources The Power BI enterprise gateway Network latency The Power BI service Which choices affect performance? Summary Chapter 2: Exploring Power BI Architecture and Configuration Understanding data connectivity and storage modes Choosing between Import and DirectQuery mode When DirectQuery is more appropriate LiveConnect mode Reaching on-premises data through gateways How gateways work Good practices for gateway performance General architectural guidance Planning data and cache refresh schedules Summary Chapter 3: DirectQuery Optimization Data modeling for DirectQuery Optimizing DirectQuery relationships Configuring for faster DirectQuery Power BI Desktop settings Optimizing external data sources Summary Section 2: Performance Analysis, Improvement, and Management Chapter 4: Analyzing Logs and Metrics Power BI usage metrics Customizing the usage metrics report Power BI logs and engine traces Activity logs and unified audit logs Analysis Services server traces with the XMLA endpoint Integration with Azure Log Analytics Monitoring Azure Analysis Services and Power BI embedded Summary Further reading Chapter 5: Desktop Performance Analyzer Technical requirements Overview of Performance Analyzer Actions and metrics in Performance Analyzer Determining user actions Spotting and mitigating performance issues Achieving consistency in tests Understanding Performance Analyzer's strengths and limitations Interpreting and acting on Performance Analyzer data Exporting and analyzing performance data Summary Chapter 6: Third-Party Utilities Technical requirements Power BI Helper Identifying large columns in the dataset Identifying unused columns Identifying bi-directional and inactive relationships Identifying measure dependencies Tabular Editor Using Tabular Editor's Best Practice Analyzer DAX Studio and VertiPaq Analyzer Analyzing model size with VertiPaq Analyzer Performance tuning the data model and DAX Summary Chapter 7: Governing with a Performance Framework Establishing a repeatable, pro-active performance improvement process The performance management cycle Knowledge sharing and awareness Helping self-service users Leveraging professional developers Approaching performance improvement collaboratively Applying the performance management cycle to different usage scenarios Summary Section 3: Fetching, Transforming, and Visualizing Data Chapter 8: Loading, Transforming, and Refreshing Data Technical requirements General data transformation guidance Data refresh, parallelism, and resource usage Improving the development experience Folding, joining, and aggregating Leveraging incremental refresh Using query diagnostics Collecting Power Query diagnostics Analyzing the Power Query logs Optimizing dataflows Summary Chapter 9: Report and Dashboard Design Technical requirements Optimizing interactive reports Controlling the visuals and associated queries Optimizing dashboards Optimizing paginated reports Summary Section 4: Data Models, Calculations, and Large Datasets Chapter 10: Data Modeling and Row-Level Security Technical requirements Building efficient data models The Kimball theory and implementing star schemas Reducing dataset size Avoiding pitfalls with row-level security (RLS) Summary Chapter 11: Improving DAX Technical requirements Understanding DAX pitfalls and optimizations The process for tuning DAX DAX guidance Summary Chapter 12: High-Scale Patterns Technical requirements Scaling with Power BI Premium and Azure Analysis Services Leveraging Power BI Premium for data scale Leveraging Azure Analysis Services for data and user scale Using partitions with AAS and Premium Scaling with composite models and aggregations Leveraging composite models Leveraging aggregations Scaling with Azure Synapse and Azure Data Lake The modern data warehouse architecture Azure Data Lake Storage Azure Synapse analytics Summary Further reading Section 5: Optimizing Premium and Embedded Capacities Chapter 13: Optimizing Premium and Embedded Capacities Understanding Premium services, resource usage, and Autoscale Premium capacity behavior and resource usage Understanding how capacities evaluate load Managing capacity overload and Autoscale Capacity planning, monitoring, and optimization Determining the initial capacity size Validating capacity size with load testing Monitoring capacity resource usage and overload Summary Chapter 14: Embedding in Applications Improving Embedded performance Measuring Embedded performance Summary Final Thoughts Index Other Books You May Enjoy Supercharge performance analytics and create repeatable patterns to ensure you get the best performance and scalability from your analytics solutions with Power BI This book comprehensively covers every layer of Power BI, from the report canvas to data modeling, transformations, storage, and architecture. Developers and architects working with any area of Power BI will be able to put their knowledge to work with this practical guide to design and implement at every stage of the analytics solution development process. This book is not only a unique collection of best practices and tips, but also provides you with a hands-on approach to identifying and fixing common performance issues. Complete with explanations of essential concepts and practical examples, you'll learn about common design choices that affect performance and consume more resources and how to avoid these problems. You'll grasp the general architectural issues and settings that broadly affect most solutions. As you progress, you'll walk through each layer of a typical Power BI solution, learning how to ensure your designs can handle scale while not sacrificing usability. You'll focus on the data layer and then work your way up to report design. We will also cover Power BI Premium and load testing. By the end of this Power BI book, you'll be able to confidently maintain well-performing Power BI solutions with reduced effort and know how to use freely available tools and a systematic process to monitor and diagnose performance problems. Data analysts, BI developers, and data professionals who have learnt the basics of Power BI and now want to understand how to build advanced analytics solutions will find this business intelligence book useful. Familiarity with the major components of Power BI and a beginner-level understanding of their purpose and use cases are required. Microsoft Power BI Performance Best Practices will help developers and solution architects make architectural design choices that ensure solutions are efficient and can perform at high user and data scale. You'll learn with the help of unique tips and tricks along with best practices that will enable you to optimize every layer of Power BI.