Automating the medallion architecture with VaultSpeed

From conceptual model to executable data products

The Medallion Architecture, with its Bronze, Silver, and Gold layers, has become a reference pattern for organizing data pipelines in modern lakehouse environments. It enforces a clean separation between raw ingestion, standardized integration, and curated consumption.

In practice, however, implementing and maintaining this architecture is complex. Every new data source or change in business definition cascades through all three layers, requiring extensive manual work in modeling, orchestration, and metadata management. The conceptual elegance of the model often breaks down under operational pressure.

VaultSpeed’s Medallion Automation

VaultSpeed's Medallion Automation closes that gap by transforming metadata into working pipelines, taking a conceptual model as input, morphing the raw sources into the Bronze layer, automating the Silver layer with Data Vault, and finally generating the Gold layer through Template Studio for analytics-ready, lineage-rich data products.

1. Starting from the conceptual model

VaultSpeed begins where architecture should: the conceptual data model.
This model, often derived from enterprise modeling tools or domain-driven designs, defines business entities, relationships, and semantics independently of implementation detail.

VaultSpeed ingests this model as metadata, preserving its structure and business meaning. It becomes the canonical definition against which all source data and downstream transformations are aligned.

This metadata-first approach ensures that every layer of the Medallion stack stays synchronized with the same semantic intent, even as new sources or domains are added over time.

2. Morphing Sources into the Bronze Layer

At the Bronze layer, the focus is on raw data ingestion. Source systems rarely align cleanly with the conceptual definitions, naming conventions differ, structures vary, and data often arrives in different shapes (tables, JSON, streams, APIs).

VaultSpeed automates the morphing of these raw sources by:

• Scanning source system metadata (schemas, tables, columns, types).

• Mapping these elements to the conceptual model using configurable pattern recognition and mapping logic.

• Storing these mappings as versioned metadata, not code.

This step effectively bridges the semantic gap between the business model and the physical data reality. When a new source is added or an existing schema changes, VaultSpeed detects and reconciles those differences automatically, regenerating the Bronze ingestion layer without manual rework.

3. Automating the silver layer with Data Vault

The Silver layer transforms raw data into an integrated, historized, and business-aligned dataset.
VaultSpeed automates this layer using the Data Vault 2.0 methodology, a modeling technique designed for scalability, auditability, and agility.

Based on the metadata derived from the conceptual model and the source mappings, VaultSpeed automatically generates:

• Hubs for unique business keys, ensuring consistent entity identification across systems.

• Links for relationships between entities, maintaining referential integrity.

• Satellites for descriptive and historical attributes, preserving time variance.

All DDLs, ETL/ELT logic, and orchestration definitions (for tools like dbt, Databricks, or Snowflake tasks) are generated directly from metadata, not hand-coded.

This creates a repeatable Silver layer, where every change in source or business definition can be propagated with a single metadata update, ensuring consistent integration and full lineage.

4. Generating the gold layer with template studio

The Gold layer delivers analytics-ready data products, dimensional models, aggregates, or domain-specific data marts.
In most implementations, this layer is where teams lose consistency: every analyst or data engineer builds their own version of “the truth.”

VaultSpeed’s Template Studio extends automation to this layer.
It allows architects to define reusable transformation templates that are Data Vault–aware, meaning they can automatically pull from hubs, links, and satellites in the Silver layer with full lineage context.

Templates define how business rules, KPI calculations, and dimensional flattening should be applied. Once defined, these templates can generate:

• Star schemas for BI tools

• Wide tables for machine learning

• Domain-specific datasets for data products

Because the templates are metadata-driven, the generated Gold models automatically inherit lineage, metadata, and auditability from the underlying Data Vault structure. No manual stitching is required.

5. Metadata-Driven lineage and adaptability

A key byproduct of this automation is end-to-end lineage.
Every table, attribute, and transformation is linked through metadata, from conceptual entity, to source field, to vault structure, to final metric.

When a business concept changes (for example, redefining “Customer” or adding a new source system), VaultSpeed can regenerate affected layers automatically, ensuring that the Medallion stack remains coherent and compliant.

This creates a truly metadata-driven Medallion architecture, one that can evolve continuously without sacrificing traceability or consistency.

Why this matters

Most data teams aspire to the Medallion ideal, few can maintain it at scale.
Manual implementation introduces drift, duplication, and inconsistent lineage. VaultSpeed’s metadata-driven automation enforces architectural discipline while keeping agility.

Key outcomes:

• Consistency, all layers derived from a single semantic source of truth.

• Auditability, Data Vault foundations ensure full history and lineage.

• Adaptability, automatic regeneration on schema or model change.

• Speed, pipeline generation and deployment in hours instead of weeks.

Conclusion

The Medallion Architecture provides a clear mental model for structuring modern data platforms.
VaultSpeed makes it operational, by combining conceptual modeling, Data Vault–based integration, and template-driven automation for data products.

The result: a self-describing, lineage-aware Medallion implementation, where conceptual definitions flow seamlessly into executable data pipelines and analytics-ready assets.

What was once a static architecture pattern becomes a living system, continuously aligned with business meaning, technical reality, and data governance requirements.