Why Lakehouse Maintenance in Microsoft Fabric Is Business-Critical—and How to Automate It for ROI

Exult Global
May 26
2 min read

Enterprises Are Drowning in Data—But Not All of It Adds Value

If you're managing data pipelines, dashboards, or analytics projects at scale, you've seen the warning signs:

Queries getting slower by the week
Tables duplicated across workspaces
Mysterious failures in Power BI refreshes
Skyrocketing Fabric Capacity Unit (CU) costs

Welcome to Lakehouse sprawl—a silent but growing operational tax on modern data estates.

The solution? Automated, intelligent Lakehouse maintenance powered by Microsoft Fabric, Semantic Link Labs, and governance frameworks from XYZ Global.

The High Costs of Neglected Lakehouses

Risk Area	Impact
Orphaned & Redundant Data	Storage waste, Fabric CU cost spikes, user confusion
Slow Query Performance	Poor decision agility, stakeholder frustration, report delays
Lack of Data Lineage	Failed audits, compliance risk, “don’t trust the numbers” syndrome
Workflow Fragility	Broken pipelines, unmonitored schema drift, dashboard failures

What Smart Lakehouse Maintenance Looks Like in Fabric

Regular audits aren’t enough. You need automated, policy-driven, AI-supported maintenance integrated into your Fabric governance lifecycle.

1. Table Lifecycle Automation

Archive or delete stale tables (based on last query date, CU usage, row count)
Auto-tag "orphaned" datasets (not referenced in any active report or pipeline)

2. Metadata + Lineage Monitoring with Purview

Track schema changes across workspaces
Visualize upstream/downstream impacts for every table
Embed lineage views inside Power BI for contextual trust

3. Semantic Link Labs Dashboards

Table health scores
Redundancy reports (by name similarity + schema fingerprint)
Time-series lineage graphs

4. CU Optimization Engine

Analyze over-partitioning and unused snapshot files
Automate migration of cold datasets to Azure Data Lake storage tiers
Power Automate + Copilot to suggest clean-up or reconfigurations

XYZ Global’s Lakehouse Maintenance Blueprint

We help enterprises move from firefighting to foresight with our Fabric-native approach:

Maintenance Area	Our Solutions
Schema Integrity	- Auto-alert on schema drift - Dynamic validation rules for lakehouse workloads
Data Cleanup	- Set custom retention rules - Schedule auto-compaction & refresh hierarchy maintenance
Governance & Access	- Role-based access + sensitivity labeling - DLP enforcement via Purview
Cost Control	- Fabric CU heatmaps - Capacity tiering workflows - Reallocate based on usage patterns

Real-World ROI from Smart Lakehouse Maintenance

Industry	Use Case	ROI Outcome
Insurance	Power BI Refresh Failures	Reduced failure rate from 12% → 0.5% after lineage auto-mapping
Manufacturing	Fabric CU Overruns	Saved $72K/year by deleting 8TB of unused snapshots + re-tiering workloads
Pharma	FDA Audit-Readiness	Enabled full dataset lineage visualization across 6 departments in 2 weeks