# Uploading data to Distributional Distributional runs on data, but our goal is to enable you to operate on data you already have available. If you are using a golden dataset to guide your development, we want you to use that to power your Development and Deployment testing. If you have actual Question-Answer pairs from production that are sitting in your data warehouse, we recommend that you to execute Production testing on that data to continually assert that your app is not misbehaving. Generally, data is organized on Distributional in the form of a parquet file full of app usages, e.g., the prompts and summaries observed in the last 24 hours. This data is then packaged up and shipped to Distributional’s API, primarily through our [SDK](/v0.25.x/reference/python-sdk.md). This could include any contextual information that can help determine if the app is behaving as desired, such as the day of the week. Prior to shipping the data to dbnl, the [dbnl Eval](/v0.25.x/reference/python-sdk/eval-module.md) library can be used to augment your data (especially text data) with additional columns for a more complete testing experience. --- # Agent Instructions: 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: ``` GET https://docs.dbnl.com/v0.25.x/to-be-deleted/stages-in-the-ai-software-development-lifecycle/data-in-distributional/uploading-data-to-distributional.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.