Case Study: Medallion Architecture Implementation for Global E-Commerce Company

Client: Global E-Commerce Retailer (anonymized)

Industry: E-Commerce / Retail

Data Scale: 3PB+ data lake, 500+ data producers, 1200+ tables

Engagement Type: Data Lake Restructuring & Medallion Architecture Implementation

The Challenge

A global e-commerce company had one of the most common problems in data engineering: they had a data lake, but they didn't have a data platform. Years of rapid growth had produced a lake with 1,200+ tables, no consistent naming conventions, no data quality enforcement, no defined ownership model, and no reliable way for consumers to know which tables were trustworthy.

The symptoms were constant. Analysts ran queries on tables that hadn't been updated in months. Data scientists trained models on datasets that were later discovered to have upstream quality issues. Business stakeholders stopped trusting dashboards because the same metric came back differently depending on which table you queried. The engineering team spent more time triaging "why is this number wrong" escalations than building new data products.

The root cause was structural: there was no architecture, just accumulation. LakeVector was engaged to implement a medallion architecture — Bronze, Silver, Gold — as the organizational and technical framework to bring order to the lake.

What Medallion Architecture Solved

Medallion architecture addresses the core data lake failure mode: the conflation of raw data, processed data, and serving data into a single undifferentiated namespace. By enforcing three distinct layers with explicit contracts between them, every table has a defined quality level, a defined owner, and a defined consumer expectation.

Bronze — raw, immutable, append-only ingestion. The historical record. No transformation, no filtering.
Silver — cleaned, validated, deduplicated, and joined. Reliable business entities. Queryable by technical users who understand the domain.
Gold — aggregated, business-logic-applied, domain-specific serving tables. Optimized for dashboards, reports, and downstream applications.

For this company, the architecture had a fourth implication that mattered as much as the technical structure: accountability. Bronze tables have owners. Silver transformations have owners. Gold tables have SLAs. The architecture made ownership unavoidable.

The Engagement

Phase 1: Lake Archaeology and Table Classification

Lakevector conducted a full audit of the existing 1,200+ table catalog — querying usage metadata, pipeline lineage, and last-updated timestamps to classify every table:

Active and trusted: well-maintained, regularly queried, owned
Active but untrustworthy: regularly queried but with known quality issues
Orphaned: not queried in 90+ days, owner unclear or departed
Duplicate: multiple tables representing the same entity with conflicting definitions

This audit produced the migration sequencing and, critically, identified 340+ tables that could be immediately deprecated or archived — removing noise before the new architecture was built.

Phase 2: Bronze Layer — Standardized Ingestion

Lakevector standardized all data ingestion into a Bronze layer pattern: every source system writes raw, schema-validated events to a Bronze Delta table via Auto Loader. No transformation, no filtering — just reliable, append-only capture with full historical retention.

Bronze tables got mandatory metadata: source system, ingestion timestamp, schema version, and owning team. Schema evolution was handled via Delta Lake's schema evolution capabilities, eliminating the ingestion failures that had been a constant source of operational noise.

Phase 3: Silver Layer — Data Quality Enforcement

The Silver layer was where the existing lake's quality problems were addressed head-on. Lakevector implemented a data quality framework using Great Expectations integrated into every Silver transformation pipeline:

Null checks on non-nullable business keys
Referential integrity validation across entity relationships
Range and distribution checks on numeric fields (catching upstream data anomalies before they reached consumers)
Deduplication logic for sources with at-least-once delivery guarantees

Failed quality checks quarantined records to a dedicated dead-letter table rather than silently corrupting the Silver output. Data owners received immediate alerts on quarantine events. For the first time, bad data was visible, tracked, and actionable rather than silently poisoning downstream consumers.

Phase 4: Gold Layer — Business-Logic Serving Tables

Gold tables were defined collaboratively with business stakeholders — taking the metric definitions that had previously lived in conflicting Looker LookML models, Tableau calculated fields, and analyst spreadsheets, and encoding them into single, authoritative Silver-to-Gold transformations.

One metric definition. One table. One source of truth.

Gold tables were registered in Unity Catalog with explicit SLA definitions and consumer-facing documentation. Business teams were given direct query access to Gold; Silver was reserved for technical users; Bronze was locked to engineering.

The Results

Tables deprecated or archived: 340+
Tables with documented owner and SLA: 0% to 100% of active catalog
Data quality check coverage: 100% of Silver layer tables
"Why is this number wrong" escalations: Reduced by 80% within 90 days
Metric definition conflicts eliminated: Core business metrics unified across all BI tools
Analyst trust in dashboards (self-reported): Significantly improved
ML model training on stale data incidents: Zero post-implementation

The Lasting Impact

The medallion architecture did not just clean up the lake — it changed how the organization thought about data. Teams now know what layer they're working in, what guarantees to expect, and who to hold accountable when something is wrong. New data sources onboard into Bronze by default, with a clear path to Silver promotion once quality standards are met. The lake grows, but it no longer deteriorates.

Why Lakevector

Lakevector has implemented medallion architectures for data platforms of every scale and maturity level. We bring the structural discipline and the hands-on engineering to turn a data lake into a data platform.

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