# Storage {% embed url="" %} Storage - Data Management {% endembed %} ## Summary * Data storage requirements for AI vary widely according to the application and the source material. * The **filesystem** is the foundational layer of storage. Its fundamental unit is a “file” — which can be text or binary, is not versioned, and is easily overwritten. * **Object storage** is an API over the filesystem that allows users to use a command on files (GET, PUT, DELETE) to a service, without worrying where they are actually stored. Its fundamental unit is an “object” — which is usually binary (images, sound files…). * The **database** is a persistent, fast, and scalable storage/retrieval of structured data. Its fundamental unit is a “row” (unique IDs, references to other rows, values in columns). * A **data lake** is the unstructured aggregation of data from multiple sources (databases, logs, expensive data transformations). It operates under the concept of “schema-on-read” by dumping everything in and then transforming the data for specific needs later. --- # 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://fall2019.fullstackdeeplearning.com/course-content/data-management/storage.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.