If a Big Data set (or smaller data) is in the form of documents, then it’s difficult to store them in a traditional schema-defined row and column database. Sure, you can create large blob fields to hold large arbitrary chunks of data, serialize and encode the document in some way, or just store them in a filesystem, but those options aren’t much good for querying or analysis when the data gets big.
MongoDB Document Database
MongoDB is a great example of a document database. There are no predefined schemas for tables (the schema is considered “dynamic”). Rather you declare “collections” and insert or update documents directly into each collection.
A document in this case is basically JSON with some extensions (actually BSON – Binary encoded JSON). It supports nested arrays and other things that you wouldn’t find in a relational database. If you are object oriented, this is fundamentally an object store.
Documents added to a single collection can vary widely from each other in terms of content and composition/structure (although an application layer above could obviously enforce consistency as happens when MongoDB is used under Rails).
MongoDB’s list of key features is a fantastic mini-tutorial in itself: Continue reading