> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/datasets-intro/data-catalogs.md). # Data Catalogs Teams cannot use data they cannot find, so the catalog is the shelf that names what exists. This page is what a data catalog is, then the tools that implement one. The same notes are in [Data Fabric](/data/engineering-intro/database-architecture-and-modeling.md#data-fabric) and [Data lineage vendors](/data/engineering-intro/data-lineage.md#data-lineage-vendors). ## Tools The catalog idea becomes concrete in a product. [Alation](https://www.alation.com/) presents itself as the Alation Intelligence Operating System, which it says global enterprises trust to get their AI right and keep it right. Its own definition of the catalog is the one this page works from: > A [data catalog](https://www.alation.com/product/data-catalog/) is a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness of data for intended uses. --- # 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://www.mlcompendium.com/data/datasets-intro/data-catalogs.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.