The Multidimensional Expressions (MDX) syntax similar to (SQL). You can even duplicate some of the functionality provided by MDX in SQL.
The principal difference between SQL and MDX is the ability of MDX to reference multiple dimensions. Although it is possible to use SQL to query cubes SSAS, MDX provides commands that are designed specifically to retrieve data as multidimensional data structures with almost any number of dimensions.
SQL refers to only two dimensions, columns and rows, when processing queries. The terms “column” and “row” have meaning in SQL syntax.
MDX, in comparison, can process one, two, three, or more dimensions in queries. Because multiple dimensions can be used in MDX, each dimension is referred to as an axis. The terms “column” and “row” in MDX are simply used as aliases for the first two axis dimensions in an MDX query; there are other dimensions that are also aliased, but the alias itself holds no real meaning to MDX. MDX supports such aliases for display purposes; many OLAP tools are incapable of displaying a result set with more than two dimensions.
In SQL, the SELECT clause is used to define the column layout for a query, while the WHERE clause is used to define the row layout. However, in MDX the SELECT clause can be used to define several axis dimensions, while the WHERE clause is used to restrict multidimensional data to a specific dimension or member.
In SQL, the WHERE clause is used to filter the data returned by a query. In MDX, the WHERE clause is used to provide a slice of the data returned by a query. While the two concepts are similar, they are not equivalent.
The SQL query uses the WHERE clause to contain an arbitrary list of items that should (or should not) be returned in the result set. While a long list of conditions in the filter can narrow the scope of the data that is retrieved, there is no requirement that the elements in the clause will produce a clear and concise subset of data.
In MDX, however, the concept of a slice means that each member in the WHERE clause identifies a distinct portion of data from a different dimension. Because of the organizational structure of multidimensional data, it is not possible to request a slice for multiple members of the same dimension. Because of this, the WHERE clause in MDX can provide a clear and concise subset of data.
The process of creating an SQL query is also different than that of creating an MDX query. The creator of an SQL query visualizes and defines the structure of a two-dimensional rowset and writes a query on one or more tables to populate it. In contrast, the creator of an MDX query usually visualizes and defines the structure of a multidimensional dataset and writes a query on a single cube to populate it. This could result in a multidimensional dataset with any number of dimensions; a one-dimensional dataset is possible, for example.
The visualization of an SQL result set is intuitive; the set is a two-dimensional grid of columns and rows. The visualization of an MDX result set is not as intuitive, however. Because a multidimensional result set can have more than three dimensions, it can be challenging to visualize the structure. To refer to such two-dimensional data in SQL, the name of a column and the unique identification of a row, in whatever method is appropriate for the data, are used to refer to a single cell of data, called a field. However, MDX uses a very specific and uniform syntax to refer to cells of data, whether the data forms a single cell or a group of cells.
Although SQL and MDX share similar syntax, the MDX syntax is remarkably robust, and it can be complex. However, because MDX was designed to provide a simple, effective way of querying multidimensional data, it addresses the conceptual differences between two-dimensional and multidimensional querying in a consistent and easily understood fashion.