Unstructured Data Storage

Unstructured data can be rich multimedia files, text documents, surveillance footage, social media engagements, and many more. Storage of such data requires systems with flexible schemas that can scale-out at minimal cost, without the need for extensive infrastructure. Some good examples of unstructured data storage are NoSQL databases, data lakes, and data warehouses

Unstructured data can be stored on-premise or in the cloud in databases, data lakes, and data warehouses. An unstructured data storage system should:

* Scale out or scale up as required.

* Be cost-effective without extensive infrastructure requirements.

* Cater to varied use cases as opposed to a single particular line of business.

* Have flexible schemas that can support various data types. 

* Provide powerful yet simple query capabilities to filter required data.

Some popular storage options for unstructured data are NoSQL databases, data lakes, and data warehouses. These provide scalability, flexible schema, and efficient data management. MongoDB Atlas is an excellent cloud database that can satisfy all the unstructured data storage requirements.

Unstructured data can be stored in NoSQL databases, data warehouses, and data lakes. All these options are available on-premise and in the cloud to suit business requirements. For example, MongoDB is one of the most popular NoSQL databases that provides efficient unstructured data storage and management, and can host data in its cloud platform, MongoDB Atlas

Data lakes store unstructured data in its native format. Unstructured data can be ingested in its raw format, along with other structured data. The main considerations while storing unstructured data in a data lake are: 

* Simplify the data ingestion process, especially for volumes as large as terra- and peta-bytes. 

* Ensure that the data can be repurposed for business use within the data lake.