Data platformsCase study
Fabric Lakehouse Implementation
RAAFA WA's first enterprise data platform: a Microsoft Fabric lakehouse that brings data from its SaaS applications together for governed Power BI reporting.
Before
- No data warehouse or lakehouse
- Data locked inside individual SaaS applications
- Rigid, native application reports
- Manual extracts and spreadsheets filling the gaps
After
- A single, governed Microsoft Fabric lakehouse
- Data from multiple SaaS applications combined
- Operational reporting in Power BI
- Monitoring of pipeline health and data freshness
01 · The challenge
Where they started
RAAFA WA is a not-for-profit organisation established by former Australian Flying Corps members. It provides retirement living, residential aged care and community services, and continues its long-standing commitment to supporting veterans and their families.
Data locked in applications
Operational data sat inside individual SaaS applications, with no way to combine it for cross-functional reporting.
Rigid native reports
The ERP and care management applications' built-in reports couldn't be customised to the organisation's evolving needs.
No central data platform
Without a warehouse or lakehouse, reporting across the organisation was impractical for business users.
Spreadsheet workarounds
Manual extracts and spreadsheets filled the gap, making reporting inconsistent and error-prone.
02 · The solution
What we built
DATA LEAGUE designed and delivered a Microsoft Fabric lakehouse, giving the organisation its first enterprise data platform.
How it fits together
- SaaS applicationsERP and care management
- Metadata-driven ELTConfiguration, not code
- Bronze, silver, goldFabric lakehouse
- Semantic modelPower BI
- ReportsGoverned and self-service
Metadata-driven ELT framework
New data sources and entities are onboarded through configuration, not custom code.
Medallion architecture
Bronze, silver and gold layers progressively cleanse, conform and curate data from the SaaS applications.
Power BI semantic layer
A governed semantic model over the gold layer gives business users trusted, self-service reporting.
Pipeline monitoring
A monitoring report shows pipeline runs, failures and data freshness at a glance.
03 · Delivery
How we delivered it
- 1
Assess
Understood the source applications, reporting needs and where the native reports fell short.
- 2
Build the foundation
Stood up the Fabric lakehouse and the metadata-driven ELT framework.
- 3
Curate the data
Shaped data through the bronze, silver and gold layers, ready for analysis.
- 4
Report and monitor
Delivered the Power BI semantic layer and the pipeline monitoring report.
04 · Results
What changed
One unified lakehouse
Data from multiple SaaS applications is combined and ready for cross-functional analysis.
Operational reporting in Power BI
Less reliance on application-native reports and manual spreadsheets.
Operational confidence
Monitoring surfaces pipeline health and data freshness at a glance.
Cheaper to grow
The metadata-driven foundation lowers the cost of adding future data sources.
05 · Technology
Built with
- Microsoft Fabric
- Lakehouse
- Medallion architecture
- Metadata-driven ELT
- Power BI
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