Performance Without Architectural Compromise
Performance has historically forced trade-offs: flexibility versus speed, freshness versus simplicity. Fabric addresses these challenges by integrating compute engines with shared storage.
Direct Lake, now a core capability, enables Power BI to query Lakehouse data directly while maintaining interactive performance and near-real-time freshness.
Analysts no longer wait for scheduled imports, and BI teams avoid managing parallel datasets for speed. Performance is delivered as part of the platform, not as a separate optimisation exercise.
Engineering Productivity as a First-Class Goal
Fabric’s fully managed SaaS approach abstracts away infrastructure overhead, freeing teams to focus on
data quality, logic, and outcomes. For organisations modernising from legacy SQL Server or complex cloud architectures, this simplification accelerates onboarding, reduces moving parts, and increases delivery velocity..
Cost Efficiency Through Consolidation
In 2026, cost efficiency is measured not just by compute pricing, but by architectural waste. Many analytics stacks still duplicate storage, transformations, and semantic models across tools. Fabric’s shared storage and unified semantic approach reduce this redundancy. By consolidating workloads into a single platform, organisations lower total cost of ownership while gaining clearer visibility into usage and consumption.
Governance and Trust at Scale
As analytics estates grow, governance becomes a limiting factor. Fabric embeds governance directly into the platform, rather than layering it on later. Centralised access control, shared semantic models, and integrated monitoring help maintain a single version of the truth across engineering and BI workloads. This native approach to governance has become a key differentiator, particularly for large and regulated organisations.
Materialised Views: Performance by Design
Materialised views allow teams to define precomputed results using SQL, reducing repeated computation for frequently queried logic. These views improve consistency, bridging the gap between flexibility and performance across Spark, SQL, and BI workloads.
In Lakehouse environments, this capability bridges the gap between flexibility and performance. Heavy joins and aggregations that once strained interactive analytics can now be served from optimised, precomputed representations. Importantly, materialised views also improve consistency.
Business logic defined once can be reused across Spark, SQL, and BI workloads, reducing duplication and the risk of divergent calculations.
Materialised views fit naturally into Fabric’s broader philosophy: performance should be a platform responsibility, not an engineering burden. Teams gain faster queries and more predictable behavior without adding operational complexity.
