iews evaluate the data in the tables underlying the view definition at the time the view is queried. It is a logical view of your tables, with no data stored anywhere else. The upside of a view is that it will always return the latest data to you. The downside of a view is that its performance depends on how good a select statement the view is based on. If the select statement used by the view joins many tables, or uses joins based on non-indexed columns, the view could perform poorly.
Materialized views are similar to regular views, in that they are a logical view of your data (based on a select statement), however, the underlying query resultset has been saved to a table. The upside of this is that when you query a materialized view, you are querying a table, which may also be indexed. In addition, because all the joins have been resolved at materialized view refresh time, you pay the price of the join once (or as often as you refresh your materialized view), rather than each time you select from the materialized view. In addition, with query rewrite enabled, Oracle can optimize a query that selects from the source of your materialized view in such a way that it instead reads from your materialized view. In situations where you create materialized views as forms of aggregate tables, or as copies of frequently executed queries, this can greatly speed up the response time of your end user application. The downside though is that the data you get back from the materialized view is only as up to date as the last time the materialized view has been refreshed.
Materialized views can be set to refresh manually, on a set schedule, or based on the database detecting a change in data from one of the underlying tables. Materialized views can be incrementally updated by combining them with materialized view logs, which act as change data capture sources on the underlying tables.
Materialized views are most often used in data warehousing / business intelligence applications where querying large fact tables with thousands of millions of rows would result in query response times that resulted in an unusable application.