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Ozessa

We build and manage reliable data systems inside your Microsoft Fabric workspace. You own everything.

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Ozessa is a trading name of OZESSA LTD.
Company No. 17120532 (England & Wales)

Registered Office:
167-169 Great Portland Street
5th Floor
London, W1W 5PF

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Insights
Strategic Overview
Microsoft Fabric vs Tableau Prep: Enterprise Engineering vs Desktop Blending
Owned by you. Built for truth.

Microsoft Fabric vs Tableau Prep: Enterprise Engineering vs Desktop Blending

Why visual ETL tools fail at organizational scale.

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FABRICENGINE
ERP
CRM
API
SQL
IoT
XLS
DeploymentInside your Microsoft tenant
Data Ownership100% yours
Lock-inNone - you keep everything
Built on Microsoft FabricEnterprise-grade architecture
Operational Depth
TL;DR / Executive Summary

Tableau Prep is designed for individual analysts, not for enterprise-wide, automated data reliability.

Desktop blending tools lack reliable version control, CI/CD, and monitoring telemetry.

Fabric provides centralized, mathematically verified engineering that prevents logic drift.

Read Time
14 MIN READ
Authority
Strategic Overview
Tier
Technical Evaluation
KEY FACTS
FACT_01

Tableau Prep workflows are often isolated on local machines, creating massive key-person dependencies.

FACT_02

Fabric pipelines are version-controlled via native Git integration.

FACT_03

Fabric separates the engineering layer from the visualization layer, ensuring data is accessible to any BI tool.

Definition

What is Visual ETL Limitations?

The inherent constraints of drag-and-drop data preparation tools when handling complex recursive logic, version control, and automated error alerting at scale.
Problem analysis
A. Symptom

Visible Signal

The primary dashboard breaks because the analyst who built the Tableau Prep flow is on vacation and the local file wasn't run.

B. Business Impact

Consequence

Fragile operations and complete lack of data governance.

C. Hidden Failure

Architecture Flaw

Using a visualization preparation tool as an enterprise data warehouse.

Root cause

Tools like Tableau Prep allow analysts to clean data quickly, but they bypass IT governance. This creates "Shadow Engineering." When logic is baked into a proprietary visual flow rather than centralized SQL or Python, the organization loses ownership of its own business rules. Microsoft Fabric centralizes this logic in a governed, auditable environment.

Our solution

The Reliability Engine Response

Our architecture mandates a reliability-first approach, injecting comprehensive markers and circuit breakers into the core transformation logic.

1

Migrating visual flows into centralized Fabric Dataflows Gen2 or PySpark notebooks.

2

Establishing a single source of truth in OneLake.

3

Decoupling business logic from the visualization tool.

Business impact
Automated Scheduling
Time Recovered
Reduced Analyst Labor
Cost Mitigation
Key-Person Dependency
Risk Exposure
Centralized Verification
Metric Accuracy
Technical Analysis

The comparison between Microsoft Fabric and Tableau Prep highlights the difference between personal productivity and organizational reliability. Tableau Prep is a fantastic tool for a single analyst trying to blend two CSV files. However, when a company relies on these flows for board-level reporting, they are running on fragile ground. These flows often lack comprehensive alerting, historical tracking, and Comprehensive Audit Trails.

At Ozessa, we migrate companies away from these fragile desktop processes. By moving the transformation logic into Microsoft Fabric, we ensure the rules governing your data are centralized, version-controlled, and mathematically verified. This eliminates Manual Data Debt and guarantees that your metrics are reliable, regardless of who is in the office.

Aleks - Lead Data Architect

Aleks

Lead Data Architect

Aleks is the Lead Data Architect at Ozessa, specializing in building mathematically verifiable data architectures on Microsoft Fabric. With a focus on long-term reliability and zero-lock-in data ownership, he engineers infrastructure that eliminates reporting risk.

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KEY TAKEAWAYS
  • ●Data engineering must be decoupled from data visualization.
  • ●Desktop tools create shadow IT vulnerabilities.
KEY DATA POINTS
Governance: Native Git Integration
Architecture: Centralized OneLake
On this page
  • Executive Summary
  • Key Facts
  • Definition
  • Problem analysis
  • Root Cause
  • Our Solution
  • Business Impact
  • Technical Analysis
  • Key Takeaways
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LIVE CALCULATOR

Calculate your manual data cost.

See exactly how much manual data work is costing your business in lost salary and operational lag before you commit to a structural fix.

Efficiency Diagnostic

Quantify your manual labor cost.

Adjust the sliders to match your team's current situation. We calculate the real operational cost of manual spreadsheet work.

12 hrs
5 people
£45,000

Automated Calculation

These figures represent the direct "Weekend Tax" on your operations. This is capital that could be redeployed into growth if manual reconciliations were automated.

Annual Operational Loss
£59,712

Your team of 5 spends approximately 60 hours per week on manual data tasks.

Weekly Loss12 hrs
Efficiency Cap30%
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Ready to fix your data?

Stop spending weekends reconciling spreadsheets. Let us build a reliable data system you own - inside your own Microsoft workspace.

DeploymentInside your Microsoft tenant
Data ownership100% yours
Lock-inNone - you keep everything
Built on Microsoft Fabric
Enterprise-grade architecture
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