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The common warehouse metamodel (CWM) defines a specification for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment. The specification is released and owned by the Object Management Group , which also claims a trademark in the use of "CWM".
The bus matrix purpose is one of high abstraction and visionary planning on the data warehouse architectural level. By dictating coherency in the development and implementation of an overall data warehouse the bus architecture approach enables an overall vision of the broader enterprise integration and consistency while at the same time dividing the problem into more manageable parts [2 ...
The Business of Data Vault Modeling (2010-11-19) by author Daniel Linstedt and co-authors Kent Graziano and Hans Hultgren ISBN 1-4357-1914-X Super Charge Your Data Warehouse: Invaluable Data Modeling Rules to Implement Your Data Vault (Data Warehouse Architecture Book 1) (2012-05-20) by Dan Linstedt and Kent Graziano ISBN 1-4637-7868-6
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.
In computing, Open Data Protocol (OData) is an open protocol that allows the creation and consumption of queryable and interoperable Web service APIs in a standard way. Microsoft initiated OData in 2007. [ 1 ]
Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...
Presto (including PrestoDB, and PrestoSQL which was re-branded to Trino) is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, [1] and allows use of multiple data sources within a query.
Data vault is designed to avoid or minimize the impact of those issues, by moving them to areas of the data warehouse that are outside the historical storage area (cleansing is done in the data marts) and by separating the structural items (business keys and the associations between the business keys) from the descriptive attributes.