Depending on the intended use, there are a number of database architectures in use. Many databases use a combination of strategies. On-line Transaction Processing systems (OLTP) often use a row-oriented datastore architecture, while data-warehouse and other retrieval-focused applications like Google's BigTable, or bibliographic database (library catalogue) systems may use a Column-oriented DBMS architecture.
Document-Oriented, XML, knowledgebases, as well as frame databases and RDF-stores (aka triple-stores), may also use a combination of these architectures in their implementation.
Finally, it should be noted that not all databases have or need a database 'schema' (so called schema-less databases).
Over many years the database industry has been dominated by General Purpose database systems, which offer a wide range of functions that are applicable to many, if not most circumstances in modern data processing. These have been enhanced with extensible datatypes, pioneered in the PostgreSQL project, to allow a very wide range of applications to be developed.
There are also other types of database which cannot be classified as relational databases.
Database system architectures are undergoing revolutionary changes. Most importantly, algorithms and data are being unified by integrating programming languages with the database system. This gives an extensible object-relational system where non-procedural relational operators manipulate object sets. Coupled with this, each DBMS is now a web service. This has huge impli-cations for how we structure applications. DBMSs are now object containers. Queues are the first objects to be added. These queues are the basis for transaction processing and workflow applica-tions. Future workflow systems are likely to be built on this core. Data cubes and online analytic processing are now baked into most DBMSs. Beyond that, DBMSs have a framework for data mining and machine learning algorithms. Decision trees, Bayes nets,...