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Advanced fact table design patterns for Gold layer modeling. Covers measure additivity classification (additive, semi-additive, non-additive), factless fact tables, accumulating snapshot facts, consolidated fact tables, header/line fact patterns,…
لغة النص الأصلي: الإنجليزية
Enterprise integration patterns for Gold layer dimensional models. Covers conformed dimensions, the enterprise data warehouse bus matrix, shrunken/rollup dimensions, conformed facts, and drill-across query patterns. Use when planning dimensions shared across…
لغة النص الأصلي: الإنجليزية
Patterns for creating clean, professional Mermaid ERD diagrams for data modeling documentation. Use when documenting Gold layer data models, creating master ERDs for complete models, domain-specific ERDs for focused views, or summary ERDs for large models…
لغة النص الأصلي: الإنجليزية
Comprehensive documentation standards for Gold layer tables including naming conventions, column descriptions, and metadata requirements. Use when creating Gold layer tables, columns, or writing documentation to ensure dual-purpose descriptions that serve…
لغة النص الأصلي: الإنجليزية
Provides production-grade patterns for Gold layer MERGE operations from Silver to Gold tables. Covers column mapping, schema evolution, SCD Type 1/2 patterns, fact table aggregation, and preventing variable naming conflicts with PySpark functions. Use when…
لغة النص الأصلي: الإنجليزية
Delta MERGE deduplication pattern for Gold layer to prevent duplicate source key errors. Use when merging from Silver to Gold tables where Silver may contain duplicate business keys due to incremental DLT streaming, CDC patterns, SCD Type 2 tracking, or…
لغة النص الأصلي: الإنجليزية
Pre-merge grain validation patterns for Gold layer fact tables. Use when creating fact table merge scripts to validate that DataFrame grain matches DDL PRIMARY KEY, prevent transaction vs aggregated confusion, and catch grain mismatches before MERGE. Includes…
لغة النص الأصلي: الإنجليزية
Canonical pattern for building an agentic tool-calling loop using a Databricks-hosted Foundation Model. Use when creating agent_loop.py or any backend that calls a Foundation Model with function-calling to orchestrate tools.
لغة النص الأصلي: الإنجليزية