> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/concepts/resources/vulcan.md).
# Vulcan
Vulcan builds governed data products on top of the engine you already use. It keeps transformation, quality, and semantics in one project. You define data once, then expose the same trusted contract to dashboards, applications, and AI agents.
### Why Vulcan exists
Most teams still work with raw tables and tribal knowledge. That works until another team, tool, or AI agent asks the same question. Then the wrong table gets picked, and the answer looks correct but is not.
A data product fixes that. It packages schema, semantics, ownership, lineage, quality, and freshness into one governed asset. Every consumer uses the same definitions and the same trust boundary.
### Why it runs above the engine
If the contract lives inside one warehouse, it stays locked there. Most teams run more than one engine. Analytics may live in Snowflake. Operations may live in Postgres. ML may run on Spark or a lakehouse.
Vulcan sits above those engines. The same data product contract reaches every consumer, regardless of where the data physically lives.
### How Vulcan works
Vulcan covers the full data product lifecycle in one stack:
1. **Input and output**\
Point a single config at your engine. Vulcan runs directly on Postgres, Snowflake, Spark, Trino, BigQuery, Databricks, Redshift, and more. No data movement happens unless you need it.
2. **Transformation**\
Build models in SQL, Python, or both in one project. Use `vulcan plan` to preview impact before execution. Use `vulcan run` to apply changes on your schedule.
3. **Quality**\
Catch issues before they reach consumers. The linter surfaces errors early. Assertions block bad rows at write time. Built-in data quality checks watch for anomalies and drift. Tests validate logic locally without warehouse cost.
4. **Semantics**\
Define dimensions, measures, segments, and metrics once. Vulcan validates them against your models and generates REST, GraphQL, and SQL APIs automatically.
This flow turns raw data into a governed interface that every consumer can use.
```mermaid
%%{init: {"theme":"base","themeVariables":{"fontFamily":"PP Neue Montreal, Inter, Helvetica Neue, Arial, sans-serif","fontSize":"14px","primaryColor":"#EDE9E5","primaryTextColor":"#242422","primaryBorderColor":"#242422","lineColor":"#242422","secondaryColor":"#D6CDC6","tertiaryColor":"#FFFFFF","clusterBkg":"#EDE9E5","clusterBorder":"#54DED1","edgeLabelBackground":"#FFFFFF"},"flowchart":{"curve":"basis","padding":12,"nodeSpacing":40,"rankSpacing":50}}}%%
graph LR
subgraph VT ["Vulcan Timeline →"]
direction LR
Engine["Engine
Postgres · Snowflake · Spark · Trino · BigQuery · Databricks"] -.-> Config
Config["Config"] -.-> Linter["Linter
Code Safety"]
Config -.-> Notify["Notifications
Fires across lifecycle"]
Macros["Macros
Variables · Functions"] -.-> Model
Tests["Tests
Logic Validation"] -.-> Model
Signals["Signals
Readiness Gates"] -.-> Model
Config --> Model["MODEL
SQL · Python Transformations"]
Model --> Audits
Audits{"Audits
Blocking Rules"} -->|pass| Checks
Audits -->|pass| Profiles
Audits -->|fail| Stop(("STOP"))
Checks["dq
Data Quality"] --> Sem
Profiles["Profiling
Understanding"] --> Sem
Sem["Semantics
Dimensions · Measures · Segments · Metrics"] --> REST["REST API"]
Sem --> GraphQL["GraphQL API"]
Sem --> MySQL["SQL API"]
end
classDef primary-teal fill:#54DED1,color:#202F36,stroke:#009293,stroke-width:1.5px,font-weight:600;
classDef dark-teal fill:#009293,color:#FFFFFF,stroke:#242422,stroke-width:1.5px,font-weight:600;
classDef ember fill:#FF5537,color:#FFFFFF,stroke:#733635,stroke-width:1.5px,font-weight:600;
classDef cream fill:#EDE9E5,color:#242422,stroke:#242422,stroke-width:1px;
classDef sandpaper fill:#D6CDC6,color:#242422,stroke:#242422,stroke-width:1px;
classDef surface fill:#FFFFFF,color:#242422,stroke:#242422,stroke-width:1px;
class Engine,Config surface;
class Linter,Macros,Tests,Signals primary-teal;
class Model,Sem dark-teal;
class Audits,Stop ember;
class Checks,Profiles sandpaper;
class Notify cream;
class REST,GraphQL,MySQL primary-teal;
style VT fill:none,stroke:none
```
One project carries a data product from source engine to governed API. The audit gate is the control point. Bad rows stop there and never reach semantics or downstream consumers.
### What you get
* One project for transformation, quality, and semantics.
* One governed contract across BI, apps, notebooks, and AI.
* One activation layer through REST, GraphQL, and SQL APIs.
### Get started
Start with the [Get Started guide](/concepts/resources/vulcan/guides/get-started.md). It walks through running the Vulcan CLI in Docker, connecting to your engine, and materializing your first models with `vulcan plan`.
After setup, the scaffold gives you ready-to-use folders for `audits/`, `dq/`, `tests/`, `models/semantics/`, and `models/metrics/`.
---
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