The database includes customers with gmail, hotmail, and yahoo email addresses in their profiles but our mapping project is tasked with retriving only results for customers with addresses. The MapForce Function Library includes built-in functions to manipulate BSON data: The MongoDB NoSQL database stores binary JSON data. Here is an expanded view of objects in the customers and transactions collections ready for NoSQL data mapping in MapForce:Īfter we drop in a JSON Schema or example JSON file for the output target, we’re ready to begin connecting objects in the database to the output. The schema may be assigned in the database for validation, or it may be an external file. Next, we can select the collections to map:Įach collection requires a JSON schema for mapping its objects. The database includes collections of BSON documents for 500 customers, 1,746 accounts, and 1,746 buy or sell transactions.ĭata mapping NoSQL databases in MapForce starts by selecting the NoSQL database from the MapForce Insert Database Connection Wizard and entering the connection parameters. The sample_analytics NoSQL database is an example hosted on the publicly accessible MongoDB Atlas cloud server, storing training data for a mock financial services application. This mapping extracts a subset of the binary JSON (BSON) data from multiple collections, combines, filters, transforms, and sorts the data, then exports the result as a single JSON document. Shown below is a complete data mapping from the NoSQL MongoDB sample_analytics database. MapForce includes functionality for inserting, extracting, filtering, and ordering NoSQL data. These databases leverage flexible JSON schemas and scale easily with large amounts of data and high user loads.Īltova MapForce has long supported data mapping all popular relational databases and now also includes native support for data mapping NoSQL databases. Two of the most popular NoSQL databases, MongoDB and Apache CouchDB, store data as collections of BSON (binary JSON) and JSON documents. NoSQL databases are non-tabular databases that store data differently than traditional databases made up of relational tables.
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