Microsoft Power BI Performance Best Practices Learn Practical Techniques for Building High-Speed Power BI Solutions
In a world dominated by data, organizations heavily rely on business intelligence tools like Power BI for deriving insights and informed decision-making. Yet, as data volumes grow and user demands increase, achieving optimal performance becomes challenging. Author Thomas LeBlanc, a seasoned Business...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
[2024]
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| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009843334706719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1: Architecture, Bottlenecks, and Performance Targets
- Chapter 1: Setting Targets and Identifying Problem Areas
- Defining good performance
- Reporting performance goals
- Setting realistic performance targets
- Considering areas that could slow you down
- Connecting data sources
- Import mode
- DirectQuery mode
- Live connection mode
- DirectLake mode
- Summary
- Chapter 2: Exploring Power BI Architecture and Configuration
- Understanding data connectivity and storage modes
- Choosing between Import, DirectQuery, and Direct Lake mode
- Import mode
- DirectQuery mode
- Direct Lake mode
- Live connection
- Deploying Power BI gateways
- How gateways work
- Good practices for gateway performance
- Sizing gateways
- General architectural guidance
- Capacities
- Planning data and cache refresh schedules
- Summary
- Chapter 3: Learning the Tools for Performance Tuning
- Technical requirements
- Overview of data engine architecture
- Import mode
- Executing a query
- Term definitions
- Learning about the performance analyzer
- Actions and metrics in the performance analyzer
- Determining user actions
- Exporting and analyzing performance data
- Using the Optimize ribbon
- Pause and Refresh visuals
- Optimization presets
- The Apply all slicers button
- Adapting external tools
- DAX Studio
- Query Diagnostics
- Tabular Editor
- Other tools
- 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
- Import from activity logs
- Analysis Services server traces with the XMLA endpoint.
- Integration with Azure Log Analytics
- Monitoring Azure Analysis Services (AAS) and PBIE
- Azure metrics for AAS
- Summary
- Chapter 5: Optimization for Storage Modes
- DirectQuery and relationships
- Optimizing DirectQuery relationships
- General DirectQuery guidance
- Power BI Desktop settings
- Optimizing external data sources
- Direct Lake semantic models
- Using Delta tables in Fabric
- On-demand loading
- Summary
- Chapter 6: Third-Party Utilities
- Technical requirements
- Exploring Power BI Helper
- Identifying large column dictionaries
- Identifying unused columns
- Identifying bidirectional and inactive relationships
- Identifying measure dependencies
- Working with Tabular Editor
- Using Tabular Editor's Best Practice Analyzer
- Tuning with DAX Studio and VertiPaq Analyser
- Analyzing model size with VertiPaq Analyzer
- Performance tuning the data model and DAX
- Summary
- Chapter 7: Performance Governance Framework
- Establishing a repeatable improvement process
- The performance management cycle
- Knowledge sharing and awareness
- Helping self-service users
- Leveraging professional developers
- Applying steps to different usage scenarios
- Using performance metrics reports
- Usage metrics report
- Fabric Capacity Metrics
- Calling REST APIs for monitoring data
- Custom connectors
- Storing REST API data
- Other resources
- 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 and joining queries
- Query folding
- Joining queries
- Refreshing incrementally
- Using Query Diagnostics
- Collecting Power Query diagnostics
- Analyzing the Power Query logs.
- Optimizing dataflows
- Gen2 destinations
- Summary
- Chapter 9: Report and Dashboard Design
- Technical requirements
- Optimizing report layout
- Too many elements in a report
- Reduce a busy report
- Reducing queries to the semantic model
- Using the small multiples option
- Interaction optimization for slicing and dicing
- Selecting a value for a slicer
- Disabling interaction when necessary
- Using Top N to limit data
- Moving slicers to the filter pane
- Optimization for dashboard and paginated reports
- Following best practices for dashboards
- Optimizing paginated reports
- Summary
- Part 4: Data Models, Calculations, and Large Semantic Models
- Chapter 10: Dimensional Modeling and Row Level Security
- Technical requirements
- Building efficient models
- The Kimball dimensional model theory
- Designing a basic star schema
- Building a single source of truth
- Reducing dataset size
- Considering many-to-many relationships and bi-directional filtering
- Using bi-directional relationships carefully
- Avoiding pitfalls with row-level security
- General guidance for RLS configuration
- Optimize relationships
- Guidance that applies to dynamic RLS
- Summary
- Chapter 11: Improving DAX
- Technical requirements
- Understanding row and filter context
- Calculated column
- Measure
- Dissecting row context
- Discovering filter context
- Improving the performance of a calculated column
- Improving filter context for a measure
- Understanding DAX pitfalls and optimizations
- Tuning DAX
- DAX guidance
- Summary
- Chapter 12: High Scale Patterns
- Technical requirements
- Scaling with capacities and Azure Analysis Services
- Leveraging Fabric for data scale
- Throttling and smoothing in Fabric capacity
- Leveraging AAS for data and user scale
- Using QSO to achieve higher user concurrency.
- Using partitions in the fact table
- Scaling with aggregations and composite models
- Leveraging composite models
- Leveraging aggregations
- Improving performance with Synapse and Fabric
- The modern data warehouse architecture (Synapse)
- ADLS
- Azure Synapse Analytics and Fabric
- Summary
- Further reading
- Part 5: Optimizing Capacities in Power BI Enterprises
- Chapter 13: Working with Capacities
- How a noisy neighbor impacts shared capacity
- Controlling capacity workloads and settings
- Capacity settings
- How capacities manage resources
- Managing capacity overload and Autoscale
- Handling peak loads in Premium capacity with Autoscale
- Capacity planning, monitoring, and optimization
- Determining the initial capacity size
- Validating capacity size with load testing
- Alert notifications
- Monitoring capacities
- Understanding the compute report page
- Summary
- Chapter 14: Performance Needs for Fabric Artifacts
- Fabric artifacts
- Delta tables
- Warehouse or lakehouse
- The Spark engine
- Using Direct Lake for data sources
- Monitoring Fabric resource consumption
- Measuring the hotness of data
- Tips for enhancements
- Load balancing
- Dataflow copy fast
- On-demand loading
- Loading data in large chunks
- Vacuum and Delta table structure
- Summary
- Chapter 15: Embedding in Web Apps
- Improving embedded performance
- Measuring embedded performance
- Summary
- Index
- Other Books You May Enjoy.
