![]() ![]() This example Pipeline demonstrates the difference in the input schema types on selecting and deselecting Support Type Extensions checkbox. Data captured from the upstream parameter.Execute only: Performs full execution of the Snap during Pipeline execution does not execute the Snap during Pipeline validation.ĭisabled: Disables the Snap and, by extension, its downstream Snaps.Validate & Execute: Performs limited execution of the Snap (up to 50 records) during Pipeline validation performs full execution of the Snap (unlimited records) during Pipeline execution.For more information, see the Input Schema Types example. Select this checkbox to enable the Snap to format/parse the Snaplogic-specific syntax indicating objects of the special types, such as byte arrays and date objects in JSON. Optional. Use this property to pass the data in the input document through to the output document and merge it under the key ' original'. The Process array property specifies whether or not the Component should take a root JSON array and write each element of the array as a JSON document. Generate a list of static documents within an array definition which can then be transformed into multiple documents upstream by using a JSON Splitter Snap. Generate static content that produces one output document. The Apache Velocity template can be used to pass dynamic values from upstream Snaps. For more information, see Schemas, JSON, Data Generation, Synthetic Data, DataGen, DSL, Dataset, Grammar, Randomization, Open Source, Data Science, REST API, PEG.The JSON text is treated as an Apache Velocity template, so you can substitute values from input documents or the pipeline parameters. Its purpose is to parse schema files and generate corresponding DSL models, effectively translating the JSON specification to a DataGen model, then using the original application as a middleware to generate the final datasets.īibTeX - Entry = This new platform builds upon its prior version and acts as its complement, operating jointly and sharing the same data layer, in order to assure the compatibility of both platforms and the portability of the created DSL models between them. The objective of this new product, DataGen From Schemas, is to expand DataGen’s use cases and raise the datasets specification’s abstraction level, making it possible to generate synthetic datasets directly from schemas. DataGen is able to parse these models and generate synthetic datasets according to the structural and semantic restrictions stipulated, automating the whole process of data generation with spontaneous values created in runtime and/or from a library of support datasets. This paper focuses solely on the JSON Schema component of the application.ĭataGen’s prior version is an online open-source application that allows the quick prototyping of datasets through its own Domain Specific Language (DSL) of specification of data models. This new version of DataGen is an application that makes it possible to automatically generate representative synthetic datasets from JSON and XML schemas, in order to facilitate tasks such as the thorough testing of software applications and scientific endeavors in relevant areas, namely Data Science. This document describes the steps taken in the development of DataGen From Schemas.
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