> 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/build/stage-2-productize/define-models-and-logic/data-models.md).

# Data models

Data models are the transformation layer of your data product. Each model file produces a physical table in your warehouse, and Vulcan manages execution order, versioning, and safe deployment automatically.

A Vulcan model has two parts:

1. **The `MODEL` block**: declares the model's name, kind, schedule, grain, documentation, and assertions.
2. **The `SELECT` query**: the transformation logic that produces the output.

The `kind` property inside the `MODEL` block determines how the table is materialized: rebuilt from scratch on every run (`FULL`), updated incrementally by time range, upserted by a unique key, or loaded from a CSV file (`SEED`). Choosing the right kind is the most important decision for each model.

***

## Pages in this section

| Page                                                                                            | What it covers                                                                                             |
| ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| [Properties](/build/stage-2-productize/define-models-and-logic/data-models/properties.md)       | All `MODEL` block properties: scheduling, grain, documentation, assertions, and storage options.           |
| [SQL models](/build/stage-2-productize/define-models-and-logic/data-models/sql-models.md)       | Writing SQL models with bronze, silver, and gold layer examples from `orders-analytics`.                   |
| [Python models](/build/stage-2-productize/define-models-and-logic/data-models/python-models.md) | Writing Python models using the `@model` decorator and `ExecutionContext`.                                 |
| [Model kinds](broken://pages/aPLn3fX52Pd3jsmKWWtU)                                              | Choosing the right materialization strategy: FULL, INCREMENTAL, VIEW, SEED, SCD Type 2.                    |
| [Statements](/build/stage-2-productize/define-models-and-logic/data-models/statements.md)       | Running SQL before or after a model executes using pre-statements, post-statements, and on-virtual-update. |
| [Macros](/build/stage-2-productize/define-models-and-logic/data-models/macros.md)               | Writing Python macros that generate SQL and make business logic reusable across models.                    |
| [Seeds](/build/stage-2-productize/define-models-and-logic/data-models/seeds.md)                 | Loading static CSV reference data as warehouse tables using `kind: SEED`.                                  |

***

## Next step

Start with [SQL models](/build/stage-2-productize/define-models-and-logic/data-models/sql-models.md) to write your first transformation, then choose a [model kind](broken://pages/aPLn3fX52Pd3jsmKWWtU) based on how your data refreshes.


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