If you're a data engineer building Data Vault on Snowflake, you already know the platform handles its part well. Elastic compute scales when you need it. Streams and Tasks give you native CDC and scheduling. Dynamic Tables handle incremental processing without external orchestration. Snowflake is a great engine for running a data vault.
The part that still takes too long is building the data vault itself. Writing the SQL for every hub, link, and satellite. Testing CDC logic source by source. Rewriting loading patterns when a schema changes upstream. It's not that the work is hard, rather that it's repetitive, and it multiplies with every source you add.
VaultSpeed automates that entire layer. And as of today, it's available as a native app on the Snowflake Marketplace!
Data Vault on Snowflake is the right foundation.
There's a reason so many enterprise teams have landed on Data Vault 2.0 running on Snowflake. The architecture gives you full history, audit trails, and the ability to load sources in parallel without conflicts. Snowflake's compute model lets you scale up for a heavy load and scale back down when it's done. Streams catch changes automatically. Tasks and Dynamic Tables keep things moving without bolting on an external scheduler.
It's a strong combination, and the industry has validated it across banking, insurance, healthcare, and beyond.
Automation with VaultSpeed unlocks its full potential.
So when the architecture is right, the questions are: how quickly can you build on it? And how well does it hold up when you're onboarding your twentieth source with your third team in a different time zone?
Think about what consistency would actually look like. Not a style guide that people mostly follow. Structural enforcement. Your CDC strategies, naming conventions, and handling of deletes are the same regardless of who onboarded the source or when. Not because everyone read the docs, but because the automation doesn't give you a way to deviate.
Think about what happens when a source schema changes. Right now, that probably means someone spends half a day figuring out what's affected, followed by a cautious rebuild. With VaultSpeed, the drift is detected automatically. Only the affected artifacts are regenerated. You deploy the delta and move on.
Think about lineage that you don't have to reconstruct. When every generated artifact traces back to source metadata by construction, audit and compliance checks become a query, not a research project.
Why general-purpose tools leave a gap
Snowflake's ecosystem has good transformation tools. But if you've tried using one for Data Vault specifically, you've probably noticed the fit isn't quite right. Most treat Data Vault as a template: one option among several modeling patterns. That works for generating a hub table, maybe. It doesn't work for managing the full lifecycle: business key resolution across sources, CDC pattern enforcement, schema evolution, delta deployments, and metadata-driven lineage across everything.
VaultSpeed was built specifically for that lifecycle. It's a metadata control plane for Data Vault, and it's the only platform like that available as a native Snowflake app.
How it works in practice
You point VaultSpeed at your sources and define what matters: business keys, relationships, rules. From there, VaultSpeed generates and manages everything that needs to happen in Snowflake. The SQL, the scheduling, the incremental loading, the change detection.
With VaultSpeed you're describing intent instead of writing code, and the platform turns that into a working vault.
Snowflake-native code, not generic SQL
VaultSpeed generates Snowflake SQL that's ready to run, DDL and DML, plus Tasks for scheduling and Dynamic Tables for incremental processing. If your team uses dbt, it generates dbt-compatible models alongside the native SQL so you can pick whichever execution path fits. Everything stays inside your Snowflake account.
There's nothing external to manage. And because the code comes from metadata rather than a human typing it out, you don't get the syntax errors and copy-paste inconsistencies that slow down manual development.
Consistency without policing
When VaultSpeed generates your vault artifacts, it applies the same patterns every time. CDC strategies, key handling, naming conventions, referential integrity — they're the same whether the source was configured today or six months from now by a different team.
Schema changes without fire drills
Something will change upstream. A column gets added, a type shifts, a table gets restructured. VaultSpeed picks up the drift, determines what's affected, and regenerates only those artifacts. You deploy the delta. Deletes, late-arriving facts, and the various CDC edge cases that normally eat up engineering time are handled as part of the standard flow.
Lineage by construction
Every artifact VaultSpeed generates is linked to its source metadata. That lineage isn't a separate process someone has to maintain. It's a byproduct of how the code was created. When someone asks "where does this satellite get its data?" the answer is already there. Same for impact analysis, audit trails, and regulatory reporting.
Multi-source integration at scale
One of the hardest parts of Data Vault is consolidating the same business concept, for example "Customer", across dozens of source systems into clean hub groups. VaultSpeed handles single-master, multi-master, and merged-master scenarios through metadata. When a new source arrives with its own version of "Customer," you configure it and the platform resolves how it fits into the existing hub group.
A native Snowflake app, not just Snowflake-compatible
VaultSpeed has always generated Snowflake-native code. What's new is that the platform itself now runs inside your Snowflake environment, built on the Native App Framework. That distinction matters more than it sounds. It means there's no separate infrastructure to provision and no VPN to configure. If you have a Snowflake account, you can be up and running in minutes.
VaultSpeed accesses only metadata. It never sees your raw data, so your security posture doesn't change. Everything it generates (SQL, Tasks, Dynamic Tables, dbt models) lives in your account. You can inspect it, version it, and run it independently. If you stopped using VaultSpeed tomorrow, your generated code would keep running. There's no runtime dependency.
For teams working with Snowflake Cortex or planning AI/ML workloads, VaultSpeed produces pipelines designed for those use cases, with Apache Iceberg support for open storage patterns.
Try it now, no commitment required!
Install the free trial from the Snowflake Marketplace and see VaultSpeed in action.
