> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/data-management/storage.md).

# Storage

{% embed url="<https://www.youtube.com/watch?v=HUYDy3NZkHU>" %}
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.


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