![]() MongoDB allows you to store data in almost any structure, and each field – even those deeply nested in subdocuments and arrays – can be indexed and efficiently searched. This flexibility is hugely useful when consolidating information from diverse sources or accommodating variations in documents over time, especially as new application functionality is continuously deployed. Optionally, schema validation can be used to enforce data governance controls over each collection. If a new field needs to be added to a document, then the field can be created without affecting all other documents in the collection, without updating a central system catalog or updating an ORM, and without taking the system offline. ![]() Fields can vary from document to document there is no need to declare the structure of documents to the system - documents are self-describing and support polymorphism. ![]() MongoDB stores data as documents in a binary representation called BSON (Binary JSON). In this way, related information can be stored together for fast query access through the rich and expressive MongoDB query language. JSON documents can store data in fields, as arrays, or even as nested subdocuments. Documents give you the ability to represent hierarchical relationships to store arrays and other more complex structures easily. MongoDB’s document data model maps naturally to objects in application code, making it simple for developers to learn and use. MongoDB: The Scalable Document Database That Has Become a Data Platform So, now that the impatient have been satisfied, the patient can take a deeper dive into MongoDB, then PostgreSQL, and then a comparison. If you are a creative SQL developer and want to push SQL to the limits by using advanced techniques for indexing, storing, and searching numerous structured data types, creating user-defined functions in a variety of languages, and tuning the database to the nth degree, you likely will be able to go further with PostgreSQL than any other RDBMS. If you want a relational database that will run complex SQL queries and work with lots of existing applications based on a tabular, relational data model, PostgreSQL will do the job. If you are a SQL shop and introducing a new paradigm will cost more than any other benefits mentioned will offset, PostgreSQL is a choice that will likely meet all your needs. If you want a multi-cloud database that works the same way in every public cloud, can store customer data in specific geographic regions, and supports the latest serverless and mobile development paradigms, MongoDB Atlas is the right choice.
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