MDX Introduction

MDX (Multidimensional Expressions) is a query language specifically designed to handle data stored in a multidimensional format, commonly used by data analysts and business intelligence professionals. It is an extension of the Structured Query Language (SQL) and enables users to access and analyze data stored in OLAP multidimensional databases.

Before MDX, SQL was the primary language used for querying relational databases. However, it was not suited for the complex calculations and analysis required in multidimensional databases. The rise of multidimensional databases led to the development of MDX in the mid-1990s, filling the gap for a query language that could support complex data analysis.

Microsoft created MDX in the late 1990s as part of its OLAP Services product. Since then, MDX has become a standard query language for OLAP databases and is supported by many OLAP products, including Microsoft Analysis Services, Oracle OLAP, and IBM Cognos TM1.

Multidimensional databases are built to manage intricate data sets that are arranged into multiple dimensions, enabling analysts to investigate the information from various perspectives. For instance, a sales database can have dimensions such as time, geography, product, and sales channel, each with various levels and members. MDX, the language used to interrogate and evaluate this data, offers a wide range of functionalities.

Using MDX, users can execute various operations on multidimensional data, such as rolling up, drilling down, slicing, dicing, and pivoting. It also supports tailored dimension members and sets, along with functions and logical operations.

MDX finds its applications in several domains, such as business intelligence, financial analysis, and data mining. Business intelligence leverages MDX to extract and analyze data from multidimensional databases, creating reports and dashboards. Financial analysis uses MDX to execute intricate calculations and models on financial data. Data mining employs MDX to discover insights and patterns from vast amounts of data.

MDX and SQL are both used for querying databases, but they have different approaches depending on the type of database they are designed for. SQL is built for relational databases, where data is organized into tables with rows and columns, while MDX is designed for multidimensional databases, where data is structured into cubes with multiple dimensions.

MDX offers several advantages over SQL when it comes to data analysis tasks. Its design makes it easier to perform calculations and aggregations on multidimensional data by allowing users to roll up, drill down, slice, and dice data across multiple dimensions. MDX also comes with a wide range of built-in functions and operators that simplify complex calculations and analyses.

When compared to SQL, MDX is stronger in terms of data analysis capabilities. SQL works well with relational databases, which organize data into tables with rows and columns, but it is less effective in handling complex data analysis tasks. MDX, on the other hand, is specifically optimized for working with multidimensional databases and excels at performing complex data analysis.

MDX offers a range of capabilities that enable users to perform complex data analysis tasks. These include:

Roll Up: MDX can aggregate data from a lower level of a dimension to a higher level. For example, it can aggregate monthly sales data into quarterly or yearly data.

Drill Down: MDX can break down aggregated data into a lower level of detail. For example, it can break down quarterly sales data into monthly or weekly data.

Slice and Dice: MDX allows users to select a subset of data based on one or more dimension members. For example, users can select data for a specific time period, product category, or sales region.

Pivot: MDX can rotate the axes of the data to view it from different angles. For example, users can view sales data by product category and time period.

Customized Dimension Members and Sets: MDX allows users to create custom sets of data based on specific criteria. For example, users can create a custom set of data that includes only high-value customers.

Functions and Logical Operations: MDX allows users to use built-in or custom functions to perform calculations or logical operations. For example, users can calculate the year-over-year growth rate of sales data.

MDX has a unique syntax that can take some getting used to. The syntax includes a range of keywords, functions, and operators that are used to construct queries. For instance, the following MDX will query revenue aggregated by year and aircraft manufacturer.:

select children([Aircraft Models].[ALL]) on rows, children([Date].[ALL]) on columns from [Airline A] where ([Measures].Revenue);

MDX offers advanced concepts that enable users to perform more sophisticated data analysis tasks. These include tuples, sets, and hierarchies.

Tuples are sets of values that define a single point in a multidimensional space. They can represent complex selections of data in MDX queries. Sets are collections of tuples that can be used to represent complex selections of data in MDX queries. Using sets, users can perform more complex analysis on multidimensional data.

Hierarchies are a series of related dimensions that are organized into a tree structure. They can be used to navigate data in a multidimensional space and perform more complex queries. By organizing dimensions into a hierarchy, users can drill down and roll up data more easily. Additionally, hierarchies can be used to define calculations and measures that are specific to a particular level of the hierarchy.

MDX is a powerful query language that is essential for data analysts and business intelligence professionals who need to analyze large volumes of data quickly and easily. By understanding the advantages and uses of MDX, users can leverage this powerful tool to extract insights and generate valuable insights from their data.


What is EuclidOLAP?
Code & Docs

Olap Web Console
Getting Started

Contact Us
Copyright © 2023 All Rights Reserved