Microsoft Fabric & OneLake Architecture
Choose lakehouse and warehouse patterns, access boundaries and lineage around how the data will be used.
Dependable data, from source to use
Microsoft Fabric Architecture & Data Engineering
Connect fragmented sources through a governed Fabric architecture, dependable pipelines and data that is ready to use.
A successful pipeline is only the start.
A refresh can finish with missing records or stale data. We connect ingestion, validation and reporting readiness, with a named owner for exceptions.
Core service capabilities
Microsoft Fabric, Data & Analytics
Data Engineering & Pipelines
Build Data Factory ingestion, SQL and Python/Spark transformations, reconciliation and recoverable exception handling.
Real-Time Intelligence
Use event ingestion and operational signals when the required response cannot wait for the next scheduled load.
Data Quality & Operations
Check freshness, completeness and business rules; make failures visible to a named owner.
Reference design, not a compulsory stack
Keep source-aligned, cleaned and business-ready data distinct. Add streaming only where the use case needs it; security and governance span every layer.
Process → Architecture → Delivery
Our methodology
- 01DiscoverAgree the outcome, the decisions and the requirements.
- 02Current stateMap how the work and data flow today, with evidence.
- 03Root causesFind what drives the delay, rework or disagreeing numbers.
- 04ApproachAgree the target design, scope and measures of success.
- 05BuildBuild the data model and reports on the agreed design.
- 06OptimiseTest at real volumes; tune speed, cost and usability.
- 07ProductioniseRelease with managed deployment, monitoring and support.
- 08GovernSet owners, access and controls so it keeps working.
Process redesignTo-be: failed data check → owner → revalidate → release A failed check never flows to reporting. The owner fixes at source or approves a documented exception, then the data is revalidated before release.
Steps in this diagram
Lanes: Pipeline (automated), Data owner, Platform team, Report consumers.
- Load lands
- Run quality + business checks
- Blocking fail?
- Hold rows, keep last good Gold
- Owner alerted with failed rows
- Fix at source / approve exception
- Revalidate
- Release to Gold
- Banner: data as at last good load
- Fresh, trusted data
Reference architectureGoverned Fabric medallion path Sources enter through Data Factory into Bronze. Quality checks pass valid data to Silver, and failed inputs are held for a named owner and revalidated. Business checks gate release to Gold, the semantic model and Power BI. Eventstream and Eventhouse form an optional, separate live path.
Components in this design
- ERP / SQL (operational DBs)
- SaaS APIs (CRM, HR)
- Files (CSV / Excel drops)
- Events (devices, apps)
- Data pipelines (Data Factory)
- Mirroring (near-real-time copy)
- Eventstream (optional live path)
- Bronze lakehouse (raw, as landed)
- Eventhouse (KQL database)
- Quality checks (schema, nulls, dupes)
- Silver lakehouse (cleaned, conformed)
- Quarantine (held for named owner)
- Business checks (reconcile to source)
- Gold warehouse (facts + dims)
- Semantic model (Direct Lake)
- Power BI (reports, apps)
- Real-time dashboard (operational signals)
Delivery: interactive Power BI sample reportData Pipeline Health Tell data owners whether each Gold table is fresh, complete and passing business rules, not only whether its pipeline finished. Delivered with named owners, role-based access, release pipelines and a controlled way to change governed measures.
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Synthetic sample data. Click any bar, point or row to cross-filter; use the tabs at the bottom to change page.
Next step
Do pipelines succeed while the numbers still can't be trusted?
Tell us what is happening. We agree priorities and scope before proposing any work.