Search results
Results From The WOW.Com Content Network
JSONiq primarily provides means to extract and transform data from JSON documents or any data source that can be viewed as JSON (e.g. relational databases or web services). The major expression for performing such operations is the SQL -like “ FLWOR expression” that comes from XQuery.
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 ...
Database schema: Complete Silverlight application (Desktop or Web) Pro*C: Inline SQL in C C Scriptcase: PHP, JavaScript Active Tier Complete application (Web/Mobile) and build or use the database schema PHP, HTML, JavaScript, Ajax,
JSONiq [11] is a query and transformation language for JSON. XPath 3.1 [12] is an expression language that allows the processing of values conforming to the XDM [13] data model. The version 3.1 of XPath supports JSON as well as XML. jq is like sed for JSON data – it can be used to slice and filter and map and transform structured data.
In OData protocol version 4.0, JSON format is the standard for representing data, with the Atom format still being in committee specification stage. For representing the data model, the Common Schema Definition Language (CSDL) is used, which defines an XML representation of the entity data model exposed by OData services.
XPath, SQL, XSLT XQuery at Wikibooks XQuery ( XML Query ) is a query and functional programming language that queries and transforms collections of structured and unstructured data , usually in the form of XML , text and with vendor-specific extensions for other data formats ( JSON , binary , etc.).
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.
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.