The MapForce database query window offers a quick place to test or refine the SQL query: The JSON schema is added to the mapping and its data elements may be mapped as either inputs or outputs: When we drop the JSON instance file into a data mapping, MapForce will automatically generate a JSON schema file from the instance, based on the JSON Draft 04 Schema ( ).
If you’re not familiar enough with JSON to type it in directly, you can also use XMLSpy to convert an XML instance to a JSON file. If you need to create a JSON instance from scratch, you can type it directly into XMLSpy, taking advantage of syntax coloring, context-sensitive entry helpers, structural marking to match brackets and braces, and other JSON editing features. Here is a view of the JSON instance file that is a model for the data mapping output:
We’ve written before about database mapping, most recently in Database Features Across the Altova Product Line, so we will focus here on the JSON component. We need two components to design the MapForce JSON data mapping and transformation to generate the purchase order: a SQL query into the database and a JSON instance document to model the purchase order. The required parts will be ordered from suppliers via a purchase order transferred in JSON format. New customer orders are logged in a sales database, and at the end of every day the components needed to assemble that day’s sales are tabulated via a query into the database. The Requirement: Here is an example of a typical need for JSON data mapping: A manufacturing company controls costs by exploiting a just-in-time assembly process with very little parts inventory on hand. For instance, MapForce can extract information from any popular database and produce a JSON file ready for transfer. MapForce supports JSON as both an input and output format for JSON data mapping and transformation.
JSON is a popular format for transferring data between systems thanks to its simple markup, small footprint, and heritage based on the JavaScript programming language.
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