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Data Vault is the foundation for modern, auditable data warehouses—but traditional implementation can be slow, orchestration complex, and deployment cycles lengthy. This session demonstrates how a metadata-driven approach with Stream2Vault (S2V) transforms that process into something fast, intuitive, and adaptable.

Starting from standardized YAML definitions of Hubs, Links, and Satellites, S2V validates, generates, and deploys production-ready code in a matter of minutes. This rapid cycle makes it easy to evolve the model, apply changes, and push them into production without friction. The walkthrough will cover:

• Their design principals - business data model first
• YAML definitions – a simple, consistent way to describe Data Vault objects.
• Commands in action – validate, generate, deploy in one streamlined workflow.
• Snowflake Dynamic Tables & monitoring – enabling near real-time processing and removing the need for complex orchestration.
• How you embed in an organization - architecture overview
• S2V by numbers - measurable outcomes from a client story

By combining rapid implementation with near real-time execution, Stream2Vault brings true agility and consistency to Data Vault projects. The session concludes with results from client implementations, highlighting the measurable gains in speed, adaptability, and operational simplicity.

Speaker Bio Viktor is a Data and Software Engineer who bridges data architecture and application development. He specializes in Data Vault automation and building efficient cloud data pipelines. As the developer of Stream2Vault (S2V), a Python-based CLI for generating and deploying Data Vault models, he demonstrates how to apply strong engineering principles to Data Vault 2.0 to build scalable, intuitive and performant