MongoDB

Combine real-time operational data, vectors, and elastic scale in a single intelligent data platform built on the document model—deploy across any cloud or your own infrastructure.

Use vector representations of your data to perform semantic search, build recommendation engines, design Q&A systems, detect anomalies, or provide context for generative AI Apps.

Optimize write performance with a document data model that maps to your application’s access patterns.

Guarantee millisecond response times at scale with a flexible document data model and rich query capabilities—including secondary indexing, joins, multi-document ACID transactions, and more.

Combine three systems—database, search engine, and sync mechanisms—into one and deliver 30%-50% faster. Build catalog and content search, in-app search, and single view into your application with MongoDB Search on Atlas.

Unify the core capabilities needed for application-driven analytics with MongoDB Atlas.

Efficiently analyze relationships between data entities in your collections for pattern discovery and intelligent predictions.

Easily build applications that leverage geospatial data with MongoDB's native support for GeoJSON and simple coordinate pairs.

It is a document-oriented database, which means that data is stored as documents, and documents are grouped in collections.

However, the documents in a single collection don't necessarily need to have exactly the same set of fields.

However, if you want to lock down your schema at a certain point, you can do so by applying validation rules to your collections.

Documents in MongoDB are stored in the BSON format, which is a binary-encoded JSON format.