Cubes are multi-dimensional data sources which have dimensions and facts (also known as measures) as its basic constituents. From a relational perspective dimensions can be thought of as master tables and facts can be thought of as measureable details. These details are generally stored in a pre-aggregated proprietary format and users can analyze huge amounts of data and slice this data by dimensions very easily. Multi-dimensional expression (MDX) is the query language used to query a cube, similar to the way T-SQL is used to query a table in SQL Server.
Simple examples of dimensions can be product / geography / time / customer, and similar simple examples of facts can be orders / sales. A typical analysis could be to analyze sales in Asia-pacific geography during the past 5 years. You can think of this data as a pivot table where geography is the column-axis and years is the row axis, and sales can be seen as the values. Geography can also have its own hierarchy like Country->City->State. Time can also have its own hierarchy like Year->Semester->Quarter. Sales could then be analyzed using any of these hierarchies for effective data analysis.
A typical higher level cube development process using SSAS involves the following steps:
1) Reading data from a dimensional model
2) Configuring a schema in BIDS (Business Intelligence Development Studio)
3) Creating dimensions, measures and cubes from this schema
4) Fine tuning the cube as per the requirements
5) Deploying the cube
SAI TOWERS, DIAGONALLY OPP TO GANGA-YAMUNA-KAVERI THEATRE
ABOVE BAJAJ MOTORCYCLES,
GP (100FT ROAD) COIMBATORE-641012
MAIL ID: firstname.lastname@example.org