MongoDB is a schemaless, document database that provides high performance, availability, and scalability. These features are not necessarily incompatible with a standards-compliant query language like SQL-92. In this section we will show various schemes that the cmdlet offers to bridge the gap with relational SQL and a document database.

Working with MongoDB Objects as Tables

The cmdlet models the schemaless MongoDB objects into relational tables and translates SQL queries into MongoDB queries to get the requested data. See Query Mapping for more details on how various MongoDB operations are represented as SQL.

Discovering Schemas Automatically

The Automatic Schema Discovery scheme automatically finds the data types in a MongoDB object by scanning a configured number of rows of the object. You can use RowScanDepth, FlattenArrays, and FlattenObjects to control the relational representation of the collections in MongoDB. You can also write Free-Form Queries not tied to the schema.

Customizing Schemas

Optionally, you can use Custom Schema Definitions to project your chosen relational structure on top of a MongoDB object. This allows you to define your chosen names of columns, their data types, and the location of their values in the collection.

Set GenerateSchemaFiles to save the detected schemas as simple configuration files that are easy to extend. You can persist schemas for all collections in the database or for the results of SELECT queries.