> For the complete documentation index, see [llms.txt](https://docs.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pentaho.com/pdc-use/pdc-10.2-use/ldc-explore-your-data-cp/data-sampling.md). # Data sampling In Data Catalog, the data sampling process is initiated without the need for a preliminary pre-analysis step. Samples are selected in a semi-random manner, with a focus on including the most recent entry in the respective table whenever possible. The overall process involves worker implementations tailored to the specific database type. Inputs for this process include essential information like REST polling endpoints, challenge tokens for authentication, database connection details for both the source and Data Catalog, and the scope of data to be ingested. The process allows for the selection of a defined number of samples, typically ranging from 20 to 50, and can optionally purge existing schema and relationships for testing purposes. The process outputs include progress updates during processing, such as estimated completion percentage and error counts, along with the final sample set sent to the database, replacing any existing samples. It's important to note that detailed status information about active workers may be viewable on a Worker page due to the potential presence of multiple concurrent workers. --- # 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://docs.pentaho.com/pdc-use/pdc-10.2-use/ldc-explore-your-data-cp/data-sampling.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.