Microsoft Fabric Platform Modernisation & BI Performance Stabilisation
Challenges
The enterprise perceived Microsoft Fabric as slow and unreliable due to long Power BI refresh times and frequent pipeline failures. In reality, the issues were driven by modelling anti-patterns, fragile pipelines, weak governance and missing operational baselines rather than Fabric limitations.
Outcome
The Fabric modernisation delivered faster analytics, cutting Power BI refresh times by 60–70% and pipeline failures by over 80%, while achieving a 99% on-time refresh SLA.
Microsoft Fabric Modernisation
Challenges
Solution
Technology Stack
Outcomes
Summary: Microsoft Fabric Modernisation Overview
The enterprise used Microsoft Fabric as its strategic analytics platform to unify its fragmented analytics infrastructure. The aim was to provide a unified platform that included data engineering, warehousing, business intelligence and governance. Previously, their Fabric deployment was successful; however, it started failing from an operational maturity standpoint during day-to-day operations. Soon after the Fabric went live, they struggled to deliver performance, reliability and governance needs, thus casting doubts on confidence in analytics.
As reliability concerns grew, Fabric adoption stalled beyond a small group of power users, limiting broader business uptake across teams.
Client Problem: Fabric Performance Challenges
Considering the challenges the enterprise faced, at first, it seemed like Fabric was slow and unreliable. In practice, Power BI dataset refreshes frequently exceeded 10–20 minutes for core business reports, even at moderate data volumes.
However, once the environment was examined more closely, a very different picture emerged:
Power BI Semantic models followed anti-patterns
Power BI models were built on wide, denormalised tables with bi-directional relationships, significantly increasing DAX execution costs as data volumes grew.
Pipelines were fragile by design
Fabric Data Factory pipelines lacked clear dependency management, retry logic, and failure isolation, causing cascading failures from minor upstream delays. As a result, data engineers spent a disproportionate amount of time manually rerunning pipelines and resolving downstream failures.
Governance was disconnected from operations
Microsoft Purview was enabled but not integrated with Fabric assets, resulting in no lineage visibility, unclear ownership and limited discoverability.
No operational baseline existed
The SLAs were not met for refresh times, pipeline completion windows, or data freshness, making issues reactive and subjective.
Storage and compute were tightly coupled
Lakehouse and warehouse workloads were intermixed without clear separation between raw, curated and serving layers, leading to inefficient scans and cache churn.
The real issue was not Fabric’s performance. It was the absence of architectural discipline, engineering best practices and operational knowledge needed to run Fabric as a platform.
How We Delivered: Our Microsoft Fabric Modernisation Approach
Many solution providers start by fixing all the incremental issues. Cloudaeon followed a unique approach by resolving the root cause. The goal was not to patch individual issues, but to reset Fabric at a platform level so it could operate predictably and at scale.
Re-architected OneLake storage
The first step was getting the fundamentals right. Clear areas for raw, curated, and serving data were added, as well as naming standards for data flow.
Repositioned Fabric Warehouse
Isolated BI-serving workloads from upstream processing, this significantly improved the performance and predictability.
Rebuilt Power BI semantic models
Implemented dimensional modelling (facts and conformed dimensions), simplified relationships, removed bi-directional filters, and optimised DAX measures.
Implemented intelligent refresh strategies
Replaced full refreshes with partitioning and incremental processing aligned to data arrival patterns.
Fabric Data Factory stabilisation
Modularised ingestion, transformation and publish stages with explicit dependencies, retries, and failure isolation.
Activated governance as a core capability
Integrated Microsoft Purview for automated lineage, sensitivity labels and ownership metadata across Fabric assets.
Operationalised the platform
Defined refresh SLAs and pipeline completion windows, supported by monitoring and validation embedded into daily operations.
Technology stack
• Microsoft Fabric (OneLake, Lakehouse, Warehouse)
• Fabric Data Factory
• Power BI & Semantic Models
• DAX optimisation patterns
• Microsoft Purview
• Fabric monitoring and refresh orchestration
Outcome
Cloudaeon modernised Microsoft Fabric that resulted in some significant and measurable outcomes:
60-70% reduction in refresh times for key datasets in Power BI
More than 80% reduction in pipeline failure rates
99% on-time refresh SLA for business-critical reports
Approximately 40% reduction in reactive engineering support effort
Restored trust in dashboards and renewed Fabric adoption across teams
~20% reduction in overall Fabric BI Ops and Data Ops costs as a result of stable pipelines, faster refresh cycles, consistently met SLAs, and reduced reactive operational effort following Cloudaeon’s.
What changed most, however, was perception. Fabric was no longer seen as slow or unreliable, it was now viewed as a scalable, reliable analytics platform that the business could trust.
POD & Managed Ops Transition
From Microsoft Fabric Modernisation to Fabric BI Ops & Data Ops
Following Microsoft Fabric modernisation, our engagement with the enterprise advanced to a focused Fabric POD for handling Power BI semantic optimisation constantly. The POD assumed responsibility for continuous semantic optimisation, pipeline monitoring and incident response. It also took ownership of governance evolution as additional business units and data domains were onboarded.
As part of the POD and managed operations model, Cloudaeon defined day-to-day Fabric maintenance activities, including data health checks, schema change management, large table cleanup, data retention policies and monitoring dashboards to identify long-running queries and excessive table scans.
Eventually, it also progressed to a managed operations service for SLA-backed Fabric BI Ops & Data Ops. This engagement truly demonstrates how any platform can become a long-term analytics foundation when it is treated not as a deployment, but as a continuously operated platform with the right engineering expertise.
If your Fabric platform feels slower or more fragile than expected, it may not be a tooling issue, but a platform one. Cloudaeon helps move Fabric from adoption to operation.