to choose a subset of tables on which to make aggregations. The best way to choose this subset and decide which aggregations to build is to monitor queries and design aggregations to match query patterns. Aggregate navigaton Having aggregate data in the dimensional model makes the environment more complex. To make this extra complexity transparent to the user, functionality known as aggregate navigation is used to query the dimensional and fact tables with the correct grain level. The aggregate navigation essentially examines the query to see if it can be answered using a smaller, aggregate table. Implementations of aggregate navigators can be found in a range of technologies: OLAP engines Materialized views Relational OLAP (ROLAP) services BI application servers or query tools It is generally recommended to use either of the first three technologies, since the benefits in the latter case is restricted to a single front end BI tool Challenges Since dimensional models only gain from aggregates on large data sets, it should be considered at what size of the data sets one should start using aggregates One can also ask oneself if a data warehouse always handles data sets that are too large for direct queries, or if it sometimes is a good idea to omit the aggregate tables when starting a new data warehouse project An open question is whether omitting aggregates in the first iteration of building a new data warehouse will make the structure of the dimensional model simpler References Data warehousing