Este curso para empresas se imparte bajo petición para tu equipo, en línea o en tu empresa, en inglés o árabe. Envía el formulario y te contactaremos para organizarlo. Solicitar esta formación
InstructorAlaa Hady MBA, AI Engineer
Socio director, Advanced Management Solutions
Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 2 days · Level: Advanced (Level 300)
Objectives
- Diagnose slow-rendering visuals, high memory consumption, and failing dataset refreshes.
- Use Performance Analyzer, DAX Studio, and Tabular Editor (Best Practice Analyzer) to pinpoint bottlenecks.
- Optimize VertiPaq compression, data types, cardinality, and DAX Storage Engine vs. Formula Engine execution.
- Implement Incremental Refresh and Partitioning strategies for large enterprise datasets.
Outline and contents
- End-to-end latency anatomy: Source → Power Query/Dataflow → VertiPaq Model → DAX Query → Visual Render
- Inside the VertiPaq in-memory engine: Value Encoding, Hash Encoding, Run-Length Encoding (RLE), and dictionary size
- Profiling models with VertiPaq Analyzer (in DAX Studio / Bravo): identifying high-cardinality column bloat (GUIDs, timestamps, floating points)
- Diagnosing report page bottlenecks with Performance Analyzer: reducing visual count and cross-filtering chatter
- Deep-dive DAX query tuning in DAX Studio: Server Timings, Storage Engine (SE) scans vs. Formula Engine (FE) callbacks (
CallbackDataID) - Refactoring slow DAX: replacing
FILTER(ALL(...))iterators, eliminating redundantIF/SWITCHbranch evaluations, and pushing logic upstream - Power Query & refresh optimization: preserving query folding, dataflow staging, and disabling unneeded auto date/time tables
- Incremental Refresh, hybrid tables, and XMLA partition management
- Running automated model audits with Tabular Editor's Best Practice Analyzer (BPA)
- Hands-on Tuning Clinic: reducing a sluggish 15-second report page to sub-second rendering
Target audience
Experienced Power BI developers, BI leads, analytics engineers, and platform administrators.
Expected outcomes
Participants can systematically profile any slow Power BI solution, shrink semantic model memory footprints by 40–80%, rewrite bottleneck DAX formulas, and enforce automated performance standards.
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