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Data Architecture

Fabric-focused architecture strategy, modelling, reusable frameworks, and implementation guidance.

Fabric Platform OneLake Lakehouse Data Factory Warehouse Semantic Model Real-Time Intelligence

Content

Subtopics

Architecture Strategy — Operating model, platform boundaries, governance, capacity, migration sequencing, and architecture decisions.
Data Modelling — Conceptual, logical, physical, dimensional, vault, lakehouse, warehouse, and semantic modelling in Fabric.
Silver Layer Modelling — Conformance, historization, schema evolution, quality, canonical entities, and Delta layout techniques.
Metadata-Driven Architecture — Configuration contracts, reusable orchestration, observability, restartability, and deployment patterns.
Event-Driven Architecture — Eventstreams, Eventhouse, Activator, idempotency, replay, state, and batch-stream boundaries.
Architecture Implementation — Reference delivery phases, environments, CI/CD, testing, security, and operational readiness.
Materialized Lake Views vs. dbt on Fabric Warehouse — Choosing between native Materialized Lake Views (declarative SELECT/PySpark transforms over lakehouse tables) and dbt-driven transformation against the Fabric Warehouse — when each fits, and how they coexist.