Once added, the Web Service Call component shows the structure of the web service. The structure can also be created or modified manually, providing complete flexibility.Īlthough the structure of the data read is always flat, a hierarchical structure can be imposed on it using theĪ simple wizard is used to define the data structure, this is automatic if a sample file is provided with headers and data, but can also be user defined or customised as required. Options provide the ability to specify theįormatting of the data (separated or fixed width fields), and separators and other options can be configured. If the sourceĭata contains headings then this structure can be created from a sample data file. The structure of the data to be read by the text data mapper component must be defined before it can be used. Once added, the Text/CSV reader shows the structure of the document.
You can also specify a location where the JSON data will be written to when the transform is executed. The JSON writer supports the Internet Engineering Task Force JSON Schema Standard.Ī simple wizard is used to add the JSON writer to the transform, you need to specify the JSON schema that describes your data. The JSON data mapper component requires a JSON Schema to describe the data being written. Once added, the JSON writer can be expanded to access the data described in the associated JSON Schema. You also need to specify the location where the JSON data will be read from when the transform is executed. If you don't have a schema file, then you can infer one from sample JSON data using the JSON Schema Inferring tool. Standard components allow the JSON data to beĪ simple wizard is used to add the JSON reader to the transform, you need to specify the JSON schema that describes your data. The JSON source document is represented in the data mapper as a hierarchical data source. The JSON reader supports the Internet Engineering Task Force JSON Schema Standard. Schema then one can be inferred from sample JSON documents. The JSON data mapper component requires a JSON Schema to describe the data being read. Once added, the JSON reader can be expanded to access the data specified in the associated JSON Schema. The wizard makes it possible to select a data source The Database writer supports Microsoft SQL Server, Oracle, MySQL, PostgreSQL, SQLite, VistaDB data sources, simply use the wizard to connect to yourĪ simple wizard is used to add the Database writer to the transform. Once added, the Database writer reads its structure from the database schema, representing related tables as child itemsĪlong with there appropriate cardinality. The wizard makes it possible to select a data sourceĪnd then select the data table or view to be used within the transform. Once a data reader and writer are connected, it is possible to start graphically building the transform, dragging data from the source to the target.Ī simple wizard is used to add the Database reader to the transform. Standard components allow the database results to be The database schema structure is then represented hierarchical in the data mapper using the relationships and The Database reader supports Microsoft SQL Server, Oracle, MySQL, PostgreSQL, SQLite, VistaDB data sources, simply use the wizard to connect to yourĭata source.
Once added, the Database reader can be expanded to access tables with constraints/relationships defined within the database. Web Services: SOAP WSDL and RESTful WADLĭata can be read and written between multiple different data sources and data targets.Text files: Fixed field flat file and CSV file.Databases: Microsoft SQL Server, Oracle, MySQL, PostgreSQL, SQLite, VistaDB.Support is provided for a wide range of data stores: Mapping data between different data formats The transform can also be executed within your own application by generating XSLT or C# source code. The transformations are ideal for converting legacy data to new data formats, or for inclusion as part of your data pipeline, transforming data between diverse, heterogeneous systems.Įach transform can be executed within the Liquid Studio environment and debugged by single stepping through them to display the internal state of the engine to be viewed, and path of execution. The transforms can map multiple data input sources to multiple data output targets while performing conditional logic, filtering, string manipulation, statistical functions and much more Liquid Studio Data Mapper provides an intuitive graphical interface for designing data transforms.
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