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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]
Examples of column-oriented formats include Apache ORC, [3] Apache Parquet, [4] Apache Arrow, [5] formats used by BigQuery, Amazon Redshift and Snowflake. Predominant examples of row-oriented formats include CSV, formats used in most relational databases , in-memory format of Apache Spark , and Apache Avro .
In computing, a materialized view is a database object that contains the results of a query.For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.
It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. [1] Google's Bigtable is one of the prototypical examples of a wide-column store. [2]
SELECT list is the list of columns or SQL expressions to be returned by the query. This is approximately the relational algebra projection operation. AS optionally provides an alias for each column or expression in the SELECT list. This is the relational algebra rename operation. FROM specifies from which table to get the data. [3]
In SQL, the data manipulation language comprises the SQL-data change statements, [3] which modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, [3] rather than the data stored within them, is considered to be part of a separate data definition language (DDL).
UPDATE table_name SET column_name = value [, column_name = value ... ] [ WHERE condition ] For the UPDATE to be successful, the user must have data manipulation privileges ( UPDATE privilege) on the table or column and the updated value must not conflict with all the applicable constraints (such as primary keys , unique indexes, CHECK ...
Type 3 (Add new attribute): A new column is created for a new value. History is limited to the number of columns designated for storing historical data. Type 4 (Add history table): One table keeps the current value, while the history is saved in a second table. Type 5 (Combined Approach 1 + 4): Combination of type 1 and type 4. History is ...