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Review and optimize data storage, data models, and semantic models in Microsoft Fabric lakehouses. Covers Delta table optimization (V-Order, Z-Order, compaction, partitioning), data model anti-pattern detection, semantic model DAX review, relationship…
Create reviewable metadata proposals for Microsoft Fabric lakehouse tables and semantic models. Covers schema analysis, concise technical and business descriptions, glossary terms, classifications, Purview-like sensitivity label proposals, PII and sensitive…
Generate and upload synthetic data to Microsoft Fabric lakehouse tables. Covers star-schema generation, realistic fake data (names, dates, IDs, transactions), configurable row counts, referential integrity between tables, Parquet export, and OneLake upload.…
Algorithm reference and self-contained PySpark notebook templates for cleaning Microsoft Fabric lakehouse tables. Covers profiling, duplicate detection, null analysis, type validation, statistics, IQR outlier detection, date format validation, Spanish DNI/NIE…
Infer and generate Power BI semantic models (TMDL format) from Microsoft Fabric lakehouse tables. Covers star-schema detection, table classification (fact/dimension), relationship inference, DAX measure generation, data type mapping, and deployment via REST…
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