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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]
Reserved words in SQL and related products In SQL:2023 [3] In IBM Db2 13 [4] In Mimer SQL 11.0 [5] In MySQL 8.0 [6] In Oracle Database 23c [7] In PostgreSQL 16 [1] In Microsoft SQL Server 2022 [2]
An SQL date-timestamp can still be useful to store in the fact table, as it allows for precise calculations. Having both the date and time of day in the same dimension, may easily result in a huge dimension with millions of rows.
The following tables compare general and technical information for a number of available database administration tools. Please see individual product articles for further information. This article is neither all-inclusive nor necessarily up to date. Systems listed on a light purple background are no longer in active development.
Views can represent a subset of the data contained in a table. Consequently, a view can limit the degree of exposure of the underlying tables to the outer world: a given user may have permission to query the view, while denied access to the rest of the base table. [2] Views can join and simplify multiple tables into a single virtual table. [2]
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.
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Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.