![]() To view the total amount of sales per city, I create a materialized view with the create materialized view SQL statement. I create a sample schema to store sales information : each sales transaction and details about the store where the sales took place. When possible, Amazon Redshift incrementally refreshes data that changed in the base tables since the materialized view was last refreshed. Refreshes can be incremental or full refreshes (recompute). After issuing a refresh statement, your materialized view contains the same data as would have been returned by a regular view. When the data in the base tables are changing, you refresh the materialized view by issuing a Amazon Redshift SQL statement “ refresh materialized view“. Instead of performing resource-intensive queries on large tables, applications can query the pre-computed data stored in the materialized view. Materialized views are especially useful for queries that are predictable and repeated over and over. Using materialized views in your analytics queries can speed up the query execution time by orders of magnitude because the query defining the materialized view is already executed and the data is already available to the database system. Data are ready and available to your queries just like regular table data. Instead of building and computing the data set at run-time, the materialized view pre-computes, stores and optimizes data access at the time you create it. A materialized view is like a cache for your view. A materialized view (MV) is a database object containing the data of a query. Today, we are introducing materialized views for Amazon Redshift. It is not possible to know if a table was created by a CTAS or not, making it difficult to track which CTAS needs to be refreshed and which is current. Furthermore, the CTAS definition is not stored in the database system. The query is executed at table creation time and your applications can use it like a normal table, with the downside that the CTAS data set is not refreshed when underlying data are updated. When performance is key, data engineers use create table as (CTAS) as an alternative. The database system must evaluate the underlying query representing the view each time your application accesses the view. Views provide ease of use and flexibility but they are not speeding up data access. When using data warehouses, such as Amazon Redshift, a view simplifies access to aggregated data from multiple tables for Business Intelligence (BI) tools such as Amazon QuickSight or Tableau. Views are frequently used when designing a schema, to present a subset of the data, summarized data (such as aggregated or transformed data) or to simplify data access across multiple tables. ![]() ![]() In a Relational Database Management Systems (RDBMS), a view is virtualization applied to tables : it is a virtual table representing the result of a database query. At AWS, we take pride in building state of the art virtualization technologies to simplify the management and access to cloud services such as networks, computing resources or object storage. ![]()
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