performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store
Need to support multiple dimensions: sales figures might have several dimensions related to location (region, country, state/province, store), time (year, month, week, day), product (clothing, men/women/children, brand, type), and more.
OLAP extracts data from multiple relational data sets and reorganizes it into a multidimensional format that enables very fast processing and very insightful analysis.
OLAP cube is an array-based multidimensional database. The top layer of the cube might organize sales by region; additional layers could be country, state/province, city and even specific store.
The drill-down operation converts less-detailed data into more-detailed data through one of two methods—moving down in the concept hierarchy or adding a new dimension to the cube.
Roll up aggregates data on an OLAP cube by moving up in the concept hierarchy or by reducing the number of dimensions.
The slice operation creates a sub-cube by selecting a single dimension from the main OLAP cube (time dimension).
The dice operation isolates a sub-cube by selecting several dimensions within the main OLAP cube.
The pivot function rotates the current cube view to display a new representation of the data—enabling dynamic multidimensional views of data.
OLAP tools are designed for multidimensional analysis of data in a data warehouse, which contains both transactional and historical data.
OLTP is designed to support transaction-oriented applications by processing recent transactions as quickly and accurately as possible.
SQL and relational database reporting tools can certainly query, report on, and analyze multidimensional data stored in tables, but performance slows down as the data volumes increase.