fabric-pbi-perf-remediate

Name: fabric-pbi-perf-remediate
Author: PatrickGallucci

16stars1forks

How to install fabric-pbi-perf-remediate?

patrickgallucci/fabric-skills/fabric-pbi-perf-remediate

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-pbi-perf-remediate

Installs into the current project.

Summary

Diagnose and resolve Power BI performance issues in Microsoft Fabric. Use when reports load slowly, DAX queries are inefficient, semantic models need optimization, DirectQuery is underperforming, capacity is throttled, visuals render slowly, or data refresh is slow. Covers Perfor

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SKILL.md

1	# Power BI Performance remediate in Microsoft Fabric
2
3	Systematic toolkit for diagnosing, analyzing, and resolving Power BI performance bottlenecks across the Microsoft Fabric platform. Covers semantic model optimization, DAX tuning, capacity management, DirectQuery diagnostics, and report design best practices.
4
5	## When to Use This Skill
6
7	- Power BI reports are slow to load or interact with
8	- DAX queries take too long to execute
9	- Semantic model refresh is slow or timing out
10	- Fabric capacity is throttled or overutilized
11	- DirectQuery reports have high latency
12	- Visuals render slowly or time out
13	- Users report intermittent performance degradation
14	- Migrating to Fabric and need to optimize for the new platform
15	- Planning capacity sizing for Power BI workloads
16	- Conducting a performance audit or health check
17
18	## Prerequisites
19
20	\| Tool \| Purpose \| Required \|
21	\| --------------------------------------- \| ----------------------------------------- \| ------------ \|
22	\| Power BI Desktop \| Performance Analyzer, DAX query view \| Yes \|
23	\| DAX Studio \| Advanced DAX profiling and server timers \| Recommended \|
24	\| Fabric Capacity Metrics App \| Capacity utilization monitoring \| Yes (admins) \|
25	\| Tabular Editor / Best Practice Analyzer \| Semantic model analysis \| Recommended \|
26	\| SQL Server Profiler \| DirectQuery trace analysis \| Optional \|
27	\| PowerShell 7+ \| Automation scripts included in this skill \| Optional \|
28
29	## Step-by-Step Workflows
30
31	### Workflow 1: Initial Performance Triage
32
33	Determine where the bottleneck lives before diving deep.
34
35	1. Reproduce the issue in Power BI Desktop with Performance Analyzer enabled
36	- View ribbon > Performance Analyzer > Start recording > Refresh visuals
37	2. Categorize each visual by its dominant cost:
38	- DAX query duration > 500ms → Investigate semantic model / DAX
39	- Visual display duration > 500ms → Investigate report design
40	- Other duration > 500ms → Investigate data source / gateway
41	3. Check capacity health using the Fabric Capacity Metrics app
42	- Look for overload (>100% utilization), throttling events, or queued operations
43	4. Route to the appropriate deep-dive workflow below
44
45	### Workflow 2: DAX Query Optimization
46
47	See [dax-optimization-patterns.md](./references/dax-optimization-patterns.md) for a comprehensive catalog of anti-patterns and fixes.
48
49	1. Capture slow DAX from Performance Analyzer (copy query from visual)
50	2. Open DAX query view in Power BI Desktop (or DAX Studio)
51	3. Run query with Server Timings enabled (DAX Studio: Server Timings tab)
52	4. Analyze the breakdown:
53	- Formula Engine (FE) time: DAX calculation overhead
54	- Storage Engine (SE) time: Data scan / retrieval overhead
55	- SE queries count: High count indicates poor query plan
56	5. Apply optimization patterns from the reference guide
57	6. Re-test and compare timings
58
59	### Workflow 3: Semantic Model Optimization
60
61	See [capacity-optimization.md](./references/capacity-optimization.md) for Fabric-specific tuning.
62
63	1. Run Best Practice Analyzer (Tabular Editor or Fabric notebook)
64	2. Address findings by priority:
65	- Remove unused columns and tables
66	- Fix incorrect data types (text dates, high-precision decimals)
67	- Replace calculated columns with calculated measures where possible
68	- Reduce cardinality on high-cardinality columns
69	3. Evaluate storage mode (Import vs DirectQuery vs Composite)
70	4. Configure incremental refresh for large fact tables
71	5. Enable VOrder for read-heavy Power BI workloads in Fabric:
72	```
73	spark.sql.parquet.vorder.default=true
74	```
75
76	Or use the `readHeavyForPBI` resource profile at the environment level.
77
78	### Workflow 4: Report Design Optimization
79
80	1. Audit visual count per page (target: 8 or fewer interactive visuals)
81	2. Identify high-cardinality visuals (tables/matrices with thousands of rows)
82	3. Check for excessive cross-filtering between visuals
83	4. Evaluate filter context complexity (many slicers, complex RLS)
84	5. Consider:
85	- Bookmarks + drill-through instead of dense pages
86	- Pre-aggregated measures instead of visual-level calculations
87	- Paginated reports for large tabular exports
88
89	### Workflow 5: DirectQuery Performance
90
91	See [directquery-tuning.m

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fabric-pbi-perf-remediate

16stars1forks

View on GitHub

How to install fabric-pbi-perf-remediate?

patrickgallucci/fabric-skills/fabric-pbi-perf-remediate

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-pbi-perf-remediate

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Power BI Performance remediate in Microsoft Fabric
2
3	Systematic toolkit for diagnosing, analyzing, and resolving Power BI performance bottlenecks across the Microsoft Fabric platform. Covers semantic model optimization, DAX tuning, capacity management, DirectQuery diagnostics, and report design best practices.
4
5	## When to Use This Skill
6
7	- Power BI reports are slow to load or interact with
8	- DAX queries take too long to execute
9	- Semantic model refresh is slow or timing out
10	- Fabric capacity is throttled or overutilized
11	- DirectQuery reports have high latency
12	- Visuals render slowly or time out
13	- Users report intermittent performance degradation
14	- Migrating to Fabric and need to optimize for the new platform
15	- Planning capacity sizing for Power BI workloads
16	- Conducting a performance audit or health check
17
18	## Prerequisites
19
20	\| Tool \| Purpose \| Required \|
21	\| --------------------------------------- \| ----------------------------------------- \| ------------ \|
22	\| Power BI Desktop \| Performance Analyzer, DAX query view \| Yes \|
23	\| DAX Studio \| Advanced DAX profiling and server timers \| Recommended \|
24	\| Fabric Capacity Metrics App \| Capacity utilization monitoring \| Yes (admins) \|
25	\| Tabular Editor / Best Practice Analyzer \| Semantic model analysis \| Recommended \|
26	\| SQL Server Profiler \| DirectQuery trace analysis \| Optional \|
27	\| PowerShell 7+ \| Automation scripts included in this skill \| Optional \|
28
29	## Step-by-Step Workflows
30
31	### Workflow 1: Initial Performance Triage
32
33	Determine where the bottleneck lives before diving deep.
34
35	1. Reproduce the issue in Power BI Desktop with Performance Analyzer enabled
36	- View ribbon > Performance Analyzer > Start recording > Refresh visuals
37	2. Categorize each visual by its dominant cost:
38	- DAX query duration > 500ms → Investigate semantic model / DAX
39	- Visual display duration > 500ms → Investigate report design
40	- Other duration > 500ms → Investigate data source / gateway
41	3. Check capacity health using the Fabric Capacity Metrics app
42	- Look for overload (>100% utilization), throttling events, or queued operations
43	4. Route to the appropriate deep-dive workflow below
44
45	### Workflow 2: DAX Query Optimization
46
47	See [dax-optimization-patterns.md](./references/dax-optimization-patterns.md) for a comprehensive catalog of anti-patterns and fixes.
48
49	1. Capture slow DAX from Performance Analyzer (copy query from visual)
50	2. Open DAX query view in Power BI Desktop (or DAX Studio)
51	3. Run query with Server Timings enabled (DAX Studio: Server Timings tab)
52	4. Analyze the breakdown:
53	- Formula Engine (FE) time: DAX calculation overhead
54	- Storage Engine (SE) time: Data scan / retrieval overhead
55	- SE queries count: High count indicates poor query plan
56	5. Apply optimization patterns from the reference guide
57	6. Re-test and compare timings
58
59	### Workflow 3: Semantic Model Optimization
60
61	See [capacity-optimization.md](./references/capacity-optimization.md) for Fabric-specific tuning.
62
63	1. Run Best Practice Analyzer (Tabular Editor or Fabric notebook)
64	2. Address findings by priority:
65	- Remove unused columns and tables
66	- Fix incorrect data types (text dates, high-precision decimals)
67	- Replace calculated columns with calculated measures where possible
68	- Reduce cardinality on high-cardinality columns
69	3. Evaluate storage mode (Import vs DirectQuery vs Composite)
70	4. Configure incremental refresh for large fact tables
71	5. Enable VOrder for read-heavy Power BI workloads in Fabric:
72	```
73	spark.sql.parquet.vorder.default=true
74	```
75
76	Or use the `readHeavyForPBI` resource profile at the environment level.
77
78	### Workflow 4: Report Design Optimization
79
80	1. Audit visual count per page (target: 8 or fewer interactive visuals)
81	2. Identify high-cardinality visuals (tables/matrices with thousands of rows)
82	3. Check for excessive cross-filtering between visuals
83	4. Evaluate filter context complexity (many slicers, complex RLS)
84	5. Consider:
85	- Bookmarks + drill-through instead of dense pages
86	- Pre-aggregated measures instead of visual-level calculations
87	- Paginated reports for large tabular exports
88
89	### Workflow 5: DirectQuery Performance
90
91	See [directquery-tuning.m

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