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
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