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Microsoft Fabric

OneLake as your
EBITDA intelligence layer

Start with finance: clean DSO/DPO/CCC, trusted close numbers, and audit-ready lineage. On that same OneLake foundation, unify operational signals like field utilization, job costing, and labor productivity, plus procurement visibility across vendor spend, leakage, and supplier risk. One architecture, finance-first, built for EBITDA decisions.

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Engagement overview
Microsoft Fabric Implementation
Timeline10 weeks
DeliverableProduction Lakehouse
Phases4 phases
Platform adoption28,000+ orgs on Fabric
Delivery ModelYour Azure tenant
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The problem

What stops data
platforms from shipping

Most Fabric projects stall after the proof of concept. The demo worked. Production didn't follow.

The proof of concept that never became production
The demo ran great on sample data. Then someone asked what happens with the actual ERP export: its 14 source systems, inconsistent schemas, and three years of patch history. That question still hasn't been answered.
Security blocked the deployment
Fabric implementation was ready to go live. Then the security review found no private endpoints, no governance, and data that technically left the network boundary. The deployment was put on hold. Indefinitely.
Notebooks no one else can maintain
A data engineer built the pipelines. They left. The notebooks have no documentation, no error handling, and no monitoring. Finance's dashboard breaks every third Monday for reasons nobody can diagnose.

Implementation Approach

Medallion layers with
a purpose at each level

Bronze preserves raw data exactly as it arrived. Silver standardizes. Gold serves the business. Each transformation is traceable. No black boxes.

Bronze
Raw data, preserved
Source data lands exactly as extracted: no transformations, no cleaning. Full audit trail from source system to landing zone. If something goes wrong downstream, you can always re-derive from bronze.
Silver
Cleaned and standardized
Schema enforcement, deduplication, null handling, type casting, and business key resolution. This layer is where "vendor_name" from SAP becomes the same entity as "Vendor Name" from QuickBooks.
Gold
Business-ready datasets
Aggregations, business metrics, and analytical models built on governed silver data. Power BI connects here. Analysts build reports here. The CFO's dashboard is driven from here.

EBITDA intelligence

One foundation. Every number that moves EBITDA.

Finance
  • DSO / DPO / CCC
  • Duplicate payments
  • Close cycle time
Operations
  • Field utilization
  • Job costing
  • Labor productivity
Procurement
  • Spend by vendor
  • Supplier risk
  • Contract leakage
Revenue
  • Billing accuracy
  • AR aging
  • Customer margin
"The same OneLake that cleans your financial data becomes the operational intelligence layer your PE sponsor is actually asking for."

What we build

Six capabilities.
One production platform.

Workspace Architecture
Dev, test, prod separation. Capacity allocation. Team boundaries. A foundation that scales without needing to be rebuilt when you grow.
Lakehouse with Medallion
Bronze raw layer, silver cleaned layer, gold business-ready layer. Each transformation traceable. Each layer purpose-built. Delta Lake format throughout, with 2026 OneLake Catalog showing full schema and item-level lineage in one view.
Pipelines That Run
Data Factory and Spark notebooks with error handling, logging, monitoring, and alerting. Now with the ODBC Driver for Fabric Data Engineering: full Entra ID auth, Spark SQL compliance, and session reuse for enterprise-grade throughput.
Purview Configuration
Data catalog, sensitivity labels, lineage tracking, access policies. Expanded DLP coverage for Fabric and Lakehouse environments now GA, configured during implementation, not as a retrofit six months later.
CMK Encryption + Private Endpoints
Notebooks now run in CMK-enabled workspaces with content encrypted at rest via Azure Key Vault, with no workflow changes. Combined with managed private endpoints: data never traverses public internet.
Composite Semantic Models
2026 public preview: mix Direct Lake OneLake tables with import tables in a single semantic model. Best of both worlds: real-time performance where it matters, import flexibility where it doesn't. Analysts build on governed data, not raw tables.

How we deliver

Ten weeks to a working foundation

Four phases. Defined outputs at each milestone. No ambiguous scope.

1
Weeks 1–2
Discovery
Document current sources and pain points. Design target architecture. Identify first 3–5 data sources. Establish governance requirements.
Architecture document + implementation roadmap
2
Weeks 3–4
Environment Setup
Provision Fabric capacity and workspaces. Configure Purview integration. Set up private endpoints. Establish dev/test/prod strategy.
Production-ready Fabric environment
3
Weeks 5–8
Pipeline Development
Build pipelines for priority sources. Implement medallion layers. Configure refresh schedules and monitoring. Validate data quality at each stage.
Data flowing into your Lakehouse
4
Weeks 9–10
Analytics Foundation
Build the semantic model. Configure row-level security. Create initial dashboards with your team. Train analysts on self-service.
Analysts running reports on governed data

Real example

PE portfolio company,
three ERPs, one close cycle

PE-backed portfolio
Three acquisitions in two years. SAP, NetSuite, and QuickBooks running in parallel. CFO needs consolidated financials. Finance team spending the first week of every month in Excel.
What we built
  • Fabric Lakehouse with unified chart of accounts across all three ERPs
  • Automated daily pipelines from SAP, NetSuite, and QuickBooks
  • Medallion architecture: raw preserved, standardized in Silver, business-ready in Gold
  • Purview lineage from source system to executive dashboard
  • Flash report dashboard the CFO checks every morning
Before the engagement
  • Manual exports from each ERP into Excel every month
  • Board meetings delayed waiting for "final" numbers that arrived with asterisks
  • VLOOKUP-based reconciliation nobody fully trusted
  • No lineage from source to report; every number was defensible only by memory
10 → 3Days to monthly close
WeeklyFlash reports for board
ZeroAsterisks on the numbers

Common questions

What people
usually ask

Should we use Fabric or stick with Databricks/Snowflake?
Depends on your stack. If you're a Microsoft shop (M365, Power BI, Azure), Fabric integrates in ways Databricks and Snowflake can't. Fabric is also SaaS; less infrastructure to manage. If you're committed to multi-cloud or have heavy Databricks investment, the answer might be different. We help you evaluate during assessment.
Lakehouse vs. Warehouse: what's the difference?
Both live in OneLake. Lakehouse uses Delta Lake format and supports SQL plus Spark notebooks, which is flexible for engineering and data science. Warehouse is pure SQL analytics, optimized for BI. Most organizations use both: Lakehouse for transformation, Warehouse for serving. They share storage.
Our data is sensitive. How does Fabric handle compliance?
Fabric integrates with Purview for governance. Sensitivity labels, access policies, and lineage are built in. For strict requirements (HIPAA, SOC 2, FedRAMP), Fabric supports private endpoints, managed VNets, customer-managed encryption keys. Your data stays in your tenant.
What happens after you leave?
You own everything. We document the architecture, train your team, and establish processes. The goal is a self-sustaining foundation, not ongoing dependency on us.
We already have Power BI. What does Fabric add?
Power BI is included: it's Fabric's BI workload. What Fabric adds is the unified foundation underneath. Direct Lake mode lets Power BI query OneLake directly without importing data. Faster refresh, less duplication, same governance across the entire stack.

Ready to unify
your data?

We'll assess your current data landscape, identify quick wins, and map out what a Fabric foundation looks like for your organization.

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