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Microsoft Power BI Performance Best Practices

Microsoft Power BI Performance Best Practices

Bhavik Merchant

Kup pozycję na ebookpoint.pl

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.

  • Microsoft Power BI Performance Best Practices
  • Foreword
  • Contributors
  • About the author
  • About the reviewers
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
    • Get in touch
    • Share Your Thoughts
  • Part 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
  • Part 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 Analyzers 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
  • Part 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
  • Part 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
  • Part 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
    • Why subscribe?
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