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BigQuery is a managed, serverless data warehouse product by Google, offering scalable analysis over large quantities of data. It is a Platform as a Service that supports querying using a dialect of SQL. It also has built-in machine learning capabilities. BigQuery was announced in May 2010 and made generally available in November 2011. [1]
In relational databases, the information schema (information_schema) is an ANSI-standard set of read-only views that provide information about all of the tables, views, columns, and procedures in a database. [1]
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.
Yes - user manager with support for database and schema permissions as well as for individual object (table, view, functions) permissions; Some - simple user manager with support for database and schema permissions; No - no user manager, or read-only user manager
Alternative views - The replica databases (used by Quarry) have copies of certain tables that are exactly the same, except they have a different primary key. Using one of these tables and properly using its corresponding primary key can speed up queries. See the "Alternative views" section below. Example: using logging_userindex instead of logging.
The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical simulations. [1] As a result of these tradeoffs, row-oriented formats are more commonly used in Online transaction processing (OLTP) and column-oriented formats are more commonly used in Online analytical processing (OLAP).
A schema crosswalk is a table that shows equivalent elements (or "fields") in more than one database schema. It maps the elements in one schema to the equivalent elements in another. It maps the elements in one schema to the equivalent elements in another.
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.