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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 ...
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.
Later it was used to refer to a subset of Structured Query Language (SQL) for declaring tables, columns, data types and constraints. SQL-92 introduced a schema manipulation language and schema information tables to query schemas. [2] These information tables were specified as SQL/Schemata in SQL:2003.
Trino is an open-source distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. [1] Trino can query data lakes that contain a variety of file formats such as simple row-oriented CSV and JSON data files to more performant open column-oriented data file formats like ORC or Parquet [2] [3] residing on different storage systems like ...
After freezing the database no more schema alterations take place. Relations among tables are mapped in the same way: by convention. For instance, to create a one-to-many relationship between two tables one assigns an array to the property bearing the name of the target table. This automatically creates the table as well as the required columns.
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.
The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects.
The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database.