Bronze: Raw & Immutable
Every source lands unchanged, with full history. If a number is ever questioned, you can trace it to the original record.
IMS SaigonBuilding a Single Source of Truth with a Modern Lakehouse
Unify data silos into a single, scalable Modern Lakehouse using Delta Lake, Databricks, or Microsoft Fabric.
- single source of truth data architecture
- Modern Lakehouse
- Delta Lake
- Databricks
Why 'One Big Database' Never Worked
Generations of enterprises tried to solve conflicting numbers by centralizing everything into a single warehouse. It failed for predictable reasons: source systems multiply, business definitions evolve faster than central teams can model them, and shadow Excel pipelines fill every gap. A modern single source of truth is not one database — it is one governed refinement path from raw data to certified business metrics.
The Medallion Lakehouse
The medallion architecture organizes data into three layers, each with a clear quality contract:
Silver: Cleansed & Conformed
Deduplicated, typed, standardized. Customer IDs match across ERP and CRM here. This is where data quality rules run and are measured.
Gold: Business-Ready
Facts and dimensions modeled for consumption. Each Gold product has an owner, an SLA, and documented definitions.
Reference Architecture
Lineage is End-to-End
From a KPI on a CFO dashboard, you can click back through Gold, Silver, and Bronze to the source transaction. Trust is verifiable, not asserted.
AI Consumes the Same Truth
Your RAG and machine-learning workloads read Gold — so AI answers are consistent with official reporting. One truth for humans and machines.
Governance Light Enough to Survive
Heavy governance boards kill data initiatives. We implement a minimal, federated model that aligns directly with the Well-Architected reliability pillars:
01
One Named Owner
Every Gold product has one named owner in the business, not in IT.
02
Catalog Definitions
Definitions live in the catalog, versioned, next to the data — not a forgotten wiki.
03
Quality Dashboards
Quality is a dashboard, not an audit: freshness, completeness, validity scores per product.
04
Lightweight Gate
Owner sign-off plus automated quality thresholds. One meeting, not a committee.
The Business Payoff
Decision Speed
Debates shift from 'whose number is right?' to 'what should we do?'
AI Readiness
LLM and analytics initiatives start on curated data instead of a swamp — typically cutting AI project data-prep time by half.
Regulatory Confidence
Auditable lineage answers compliance questions in minutes.
Make Your Numbers Agree
Our data architects have modernized data estates from legacy Hadoop clusters to governed lakehouses for logistics, manufacturing, and retail clients. Book a data architecture assessment and get a prioritized roadmap to your first certified data products.
Frequently asked questions
How is a lakehouse different from a data warehouse?
A lakehouse stores data in open formats on cheap cloud storage while providing warehouse-grade SQL, governance, and performance — one platform for BI, data science, and AI instead of separate silos.
How long does it take to establish a single source of truth?
The first certified data product (for example, sales) is achievable in 6–8 weeks. SSOT then grows product by product; it is a program, not a big bang.
Do we need Microsoft Fabric?
Fabric is our default on Azure because storage (OneLake), engineering, and Power BI share one governed foundation. The same architecture also works with Databricks and Synapse.
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