> 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-1-discover/explore-products.md). # Explore products Use this page to learn how to discover existing Data Products in DataOS before starting new development. By discovering products first, you can: * Reuse trusted outputs that already exist. * Understand available domains, owners, and use cases. * Inspect product quality, freshness, lineage, and run status. * Decide whether to consume, extend, or build a new Data Product. * Find inputs for a downstream Data Product. * Explore the data product in Studio. *** Use the **Data Product Hub** to discover curated Data Products, Metrics, and Perspectives across the organization. Each product gives you context about what it does, who owns it, what assets it contains, how fresh and reliable it is, and how to consume it. **In DataOS, the Products area is the entry point for discovering and understanding Data Products.**
### Search or browse available products Use filters to explore available products by domain, use case, owner, tier, or tag. Shortlist products that appear relevant to your use case. Select a data product of interest.
### Product details page When you open a Data Product, the details page gives you a full view of the product. The page is organized around the key questions users typically ask when evaluating a product: | Question | Where to look | | --------------------------------------- | -------------------------------------------------------- | | What does this product do? | Product summary and description | | Can I trust it? | Quality, freshness, and readiness signals | | What does it contain? | Inputs, models, semantic model, metrics, and connections | | What business outcomes does it support? | Business outcomes section | | How can I use it? | Activation options | | Who is using it? | Usage, performance, perspectives, and apps | | Who should I contact? | Product members section | *** ### Product summary Use this section as your first checkpoint. If the description, owner, status, quality, and freshness look relevant, continue exploring the product in more detail.
*** ### Overview The **Overview** tab gives you a guided summary of the Data Product. It is divided into sections that move you from basic understanding to trust, usage, and activation. You can review: | Asset | Description | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Inputs | Source datasets or upstream assets used by the product. | | Models | Data models (outputs) created or managed as part of the product. | | Semantic model |

Business-friendly definitions, relationships, dimensions, and measures.

From a Data Product page, select Explore → Explore in Studio. Studio opens with the product's semantic models loaded in the left panel.

| | Metrics | Curated metrics exposed by the product. | | Connections | Available ways to connect to or consume the product. | Use this section to understand the product's structure.
*** ### What it answers This section highlights the metrics this Data Product measures and opens them in Studio in one click. It takes you past the technical assets and shows why the product exists. Use this section to answer: * Which business questions does this product help answer? * What outcomes or KPIs does it support? * Does it align with my use case? * Can this product help me avoid creating something new?
*** ### Trust and freshness Use this section to evaluate whether the product is trustworthy and ready to use. Useful before using the product in analysis, reporting, applications, or downstream Data Product development. You can review: | Signal | Description | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | | Quality | Shows configured quality rules and whether they are passing. | | Data freshness | Shows when the product was last refreshed and its run history of the past 10 runs. | | AI-readiness | Indicates whether the product has the required context to support AI-assisted discovery, prompts, semantic usage, or agent-based workflows. | Ask yourself: * Are the quality rules passing? * Has the data been refreshed recently? * Is the product ready for AI-assisted workflows? * Are descriptions, prompts, and semantic context available? * Should I contact the owner before using this product?
*** ### How to activate Use this section to choose the right path based on your workflow. For example: * Analysts can [connect through BI tools](/build/readme/data-connection.md). * Developers can [build with APIs](/build/stage-3-publish/deploy-data-product.md). * Data users can [query through a DB client](/build/stage-1-discover/explore-data.md). * AI workflow builders can [connect through MCP](/build/get-started/build-journey-with-ai.md). Depending on the product configuration, you see activation options such as:
*** ### How it is being used Use this section to understand whether the product is actively used and whether others already depend on it. You can review: | Element | What it shows | | ------------ | ------------------------------------------ | | Usage | Active users, new users, and repeat usage. | | Performance | Query volume and query time. | | Perspectives | Perspectives created from the product. | | Apps | Applications built using the product. |
*** ### Contact members Use the contact section when you need more context or support. Reach out to product members or raise an issue directly on the issue tracker when: * You need access or clarification. * You want to reuse the product. * You want to build a downstream product using it. * Quality or freshness signals need attention. * You need help understanding metrics, semantic definitions, or business logic. * You want to confirm whether the product is safe for production use. *** ### Recommended exploration flow Use this flow when evaluating a Data Product.
*** ### Decide what to do next After exploring the product, choose the next action. | Decision | Choose this when | | --------------------------- | -------------------------------------------------------------------------------- | | Use the Data Product | It already satisfies your use case. | | Build on the Data Product | It provides useful inputs, metrics, or semantic definitions but needs extension. | | Connect to the Data Product | You are ready to consume it through MCP, BI tools, APIs, or SQL clients. | | Inspect lineage or activity | You need more context about dependencies or recent changes. | | Contact the owner | You need clarification, access, or confirmation before using it. | | Explore more products | The product is related but not sufficient. | | Create a new Data Product | No existing product meets your requirements. | *** --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://v2.dataos.info/build/stage-1-discover/explore-products.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.