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The 5.0 version of the program was released in 2002, adding a XSLT processor, XSLT debugger, a WSDL editor, HTML importer, and a Java as well as C++ generator. The version's XML document editor was redesigned to allow for easier use by businesses. [7] XMLSpy 2006 was given the Platinum Award by SQL Pro Magazine's Editor's choice awards. [8]
SQL schema. See also: mw:Manual: ... in json format. ... ) was an experimental program set up by User:Alfio to generate html dumps, inclusive of images, search ...
Cubes contains a SQL query generator that translates the reporting queries into SQL statements. The query generator takes into account topology of the star or snowflake schema and executes only joins that are necessary to retrieve attributes required by the data analyst. The SQL backend uses SQLAlchemy Python toolkit to construct the queries.
JSON: No Smile Format Specification: Yes No Yes Partial (JSON Schema Proposal, other JSON schemas/IDLs) Partial (via JSON APIs implemented with Smile backend, on Jackson, Python) — SOAP: W3C: XML: Yes W3C Recommendations: SOAP/1.1 SOAP/1.2: Partial (Efficient XML Interchange, Binary XML, Fast Infoset, MTOM, XSD base64 data) Yes Built-in id ...
JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. It provides a contract for the JSON data required by a given application and how that data can be modified. [29] JSON Schema is based on the concepts from XML Schema (XSD) but is JSON-based. As in XSD, the same ...
A DB schema based on JSONB always has fewer tables: one may nest attribute–value pairs in JSONB type fields of the Entity table. That makes the DB schema easy to comprehend and SQL queries concise. [31] The programming code to manipulate the database objects on the abstraction layer turns out much shorter. [32]
Sphinx, like classic SQL databases, works with a so-called fixed schema, that is, a set of predefined attribute columns. These work well when most of the data stored actually has values: mapping sparse data to static columns can be cumbersome.
Semi-structured data [1] is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data.