> 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/productize.md). # Productize - [Productize the Data](https://v2.dataos.info/build/productize/overview.md): "Productize" is the P in ELP: define the transform, semantic model, quality contract, and access policy as a single object, then deploy it. - [Init and configuration](https://v2.dataos.info/build/productize/init-and-configuration.md): Scaffold the project and set config.yaml and usage.yaml. - [Connect engine](https://v2.dataos.info/build/productize/connect-engine.md): Wire your project to the engine that runs transforms and lands outputs. Config only; the full per-engine manual is in References. - [Snowflake](https://v2.dataos.info/build/productize/connect-engine/snowflake.md): Configure Snowflake as the engine for your Data Product. - [Databricks](https://v2.dataos.info/build/productize/connect-engine/databricks.md): Configure Databricks as the engine for your Data Product. - [Spark](https://v2.dataos.info/build/productize/connect-engine/spark.md): Configure Apache Spark as the engine for your Data Product. - [Trino](https://v2.dataos.info/build/productize/connect-engine/trino.md): Connect Vulcan to Trino for federated SQL across catalogs and data lakes. - [Dedicated Trino](https://v2.dataos.info/build/productize/connect-engine/trino/dedicated-trino.md): Ship a Trino cluster as part of the Data Product. Vulcan provisions and owns one cluster per product. - [Minerva](https://v2.dataos.info/build/productize/connect-engine/trino/minerva.md): Connect Vulcan to DataOS Minerva, a shared Trino-compatible cluster managed by DataOS. - [External Trino](https://v2.dataos.info/build/productize/connect-engine/trino/external-trino.md): Connect Vulcan to your own Trino-compatible endpoint, such as Starburst or self-hosted Trino. - [Postgres](https://v2.dataos.info/build/productize/connect-engine/postgres.md): Configure PostgreSQL as the engine for your Data Product. - [SQL Server](https://v2.dataos.info/build/productize/connect-engine/sql-server.md): Configure Microsoft SQL Server as the engine for your Data Product. - [Microsoft Fabric](https://v2.dataos.info/build/productize/connect-engine/microsoft-fabric.md): Configure Microsoft Fabric Warehouse as the engine for your Data Product. - [Assets](https://v2.dataos.info/build/productize/assets.md): The components you create for a Data Product, and the order to build them. - [Inputs](https://v2.dataos.info/build/productize/assets/inputs.md): Declare the upstream tables your models read from, as external models. - [Seeds](https://v2.dataos.info/build/productize/assets/seeds.md): Load small static CSV reference data as warehouse tables with SEED models. - [Data model](https://v2.dataos.info/build/productize/assets/data-model.md): Write the transforms that turn raw inputs into reliable output tables. - [Semantic model](https://v2.dataos.info/build/productize/assets/semantic-model.md): Map physical columns to business measures, dimensions, segments, and joins so people and AI can query consistent definitions. - [Metrics](https://v2.dataos.info/build/productize/assets/metrics.md): Combine a measure with a time column into a queryable time-series metric for dashboards and APIs. - [Data quality](https://v2.dataos.info/build/productize/data-quality.md): Add DQ checks that monitor quality over time and profile columns. - [Assertions](https://v2.dataos.info/build/productize/assertions.md): Block bad data at materialization. Audits define the rule; assertions attach it to a model and stop the run if it fails. - [Unit Test](https://v2.dataos.info/build/productize/unit-test.md): Unit tests verify a model's SQL logic against mock fixtures, independent of any warehouse, before assertions or a real run ever see it. - [Governance](https://v2.dataos.info/build/productize/governance.md): Control how the product runs and who can see which rows and columns. Access policy is created with the product, not bolted on. - [Plan & run](https://v2.dataos.info/build/productize/plan-and-run.md): Validate the whole project locally with vulcan info, test, evaluate, and plan before anything ships. - [Git & deploy](https://v2.dataos.info/build/productize/git-and-deploy.md): Deploy the Vulcan resource to DataOS, and verify the product is live and discoverable. - [Monitor & delete](https://v2.dataos.info/build/productize/monitor-and-delete.md): Monitor a deployed Vulcan Data Product with the DataOS CLI, read its runtime container groups, and delete the deployed resource when it's no longer needed. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://v2.dataos.info/build/productize.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.