Examining different data access methods: OLAP and data mining

Learn about the different types of data access methods including OLAP and data mining, find out about using query languages to access data warehouses and SQL and OLAP's role in accessing data.

Can you please explain the implementation of query language and OLAP technology in data warehouse and data mining? 

OLAP and data mining are various data access methods, which use query language to access data in a data store. One such data store is a data warehouse, which is usually hosted on a relational database management system (DBMS).

The primary query language in use today is structured query language (SQL). Most data access tools are simply SQL generators. Though SQL could potentially get rewritten/optimized by the DBMS, effective SQL generation by the tool still is extremely important to the efficacy of the tool. This is an area well worth exploring with data access tools during selection, in addition to the user interface and other factors.

OLAP is almost meaningless as a concept. Historically, it meant access to dimensional data stores, which are organized as metrics surrounded by "dimensions." The data access was really "built in" to the data model of the data store. It's not to be confused with MOLAP, or multidimensional OLAP, which is a physical implantation of a denormalized dimensional data model.

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