MDX using UST QUBZ

Introduction

MDX stands for Multidimensional Expressions, a query language designed for OLAP (Online Analytical Processing) systems. MDX allows users to analyze and manipulate data stored in multidimensional databases, which are designed to handle large volumes of data and complex queries.

UST Qubz is a Next Generation Data platform that enables businesses to operationalize analytics. UST Qubz has been developed to address the most common challenges in leveraging the data lake/mart or warehouses for analytical purposes by creating Aggregated Summary Multidimensional Cubes that yield performant and scalable access to our data while abstracting all intensive coding and consolidating several technologies with easy-to-use user interface. UST Qubz’s blazing-fast performance on petabyte-scale data, compared to other solutions, allows it to be applied to real-time and batch-based analytics. UST Qubz drives significant business outcomes by delivering BI that’s actionable, cost-effective, and efficient. UST Qubz aids organizations in performing interactive multi-dimensional analysis on large volumes of data.

UST Qubz supports ANSI SQL, including MDX, and is designed to handle petabyte-scale data sets.

In this article, we will explore how to use MDX with UST Qubz to analyze large-scale data sets.

Prerequisites

Before we dive into MDX and UST Qubz, let’s make sure we have everything we need. To follow along with this tutorial, you’ll need:

1. A working installation of UST Qubz
2. A data source with multidimensional data
3. Basic knowledge of SQL and OLAP concepts
4. An MDX client, such as Excel

Getting Started with UST Qubz

Assuming you have a working installation of UST Qubz, the first step is to load data into Qubz’s cube. You can do this by creating a new data model and cube in Qubz, defining dimensions, measures, and importing data from your data source by building the cube.

Once your cube build job is completed in Qubz, you can start querying the cube using SQL or MDX. In this article, we’ll focus on MDX queries.

MDX Queries in UST Qubz

MDX queries are used to retrieve data from multidimensional databases. MDX syntax is similar to SQL but with some key differences. Let’s take a look at an example MDX query:

SELECT [Measures].[Sales Amount] ON COLUMNS,
[Product].[Category].[Category] ON ROWS
FROM [Sales]

This MDX query retrieves sales amounts by product category from the Sales cube. The query has two axes: the columns axis (which displays the Sales Amount measure) and the rows axis (which displays the product category dimension).

In UST Qubz, you can execute MDX queries using an MDX client. The results of the MDX queries can be displayed in various formats, such as tables, charts, and graphs.

Analysis in Excel using UST Qubz’s MDX Service

1. Select Data → from Analysis Services

2. Next, you need to enter the server details for MDX service from Qubz. The username and password will be Qubz’s login credentials.

3. After entering the right details, Excel will be connected to the dataset.

4. Now you can analyze MDX for UST Qubz’s dataset using Excel PivotTable.

Conclusion

MDX is a powerful query language that allows users to analyze and manipulate multidimensional data stored in OLAP databases. UST Qubz is a Next Generation Data platform that provides a SQL interface for OLAP analysis, including support for MDX queries. By using MDX with UST Qubz, you can perform fast, efficient, and scalable OLAP analysis on large-scale data sets.

In this article, we have covered the basics of MDX queries in UST Qubz, including how to load data into Qubz’s cube and execute MDX queries using an MDX client. We hope this tutorial has provided you with a good starting point for using MDX with UST Qubz.

Thank you for reading this article.