This article will zoom in on OLAP cubes as a technology option for Business Intelligence tasks – and how they compare to your other BI data store choices.
Let’s dive into the topic of OLAP cubes. With data steadily growing in size and significance, Business Intelligence (BI) data store solutions are becoming more and more popular – and are dynamic options for today’s companies, corporations, and organizations to access, house, and order their company information for financial reporting, budgeting, data visualizing, as well as financial consolidations. Whether you depend on a data warehouse, a data mart, or an OLAP cube, pulling data doesn’t make the Enterprise Resource Planning (ERP) system sluggish, and you can query diverse data types to expand and enrich your analytics. In this article, we’ll take a closer look at OLAP cubes. Who can manage the technology? What exactly are they? When should you get an OLAP cube? Where are they staged? Why would you go with an OLAP cube over another BI data store option?
Let’s start with a definition. OLAP stands for online analytical processing, and the cube is in reference to the structure. To be more specific, OLAP cubes consist of measures, things you can add or count. These measures are segmented by dimensions that are attributes. In an OLAP cube, data, or measures, are organized by dimensions. To manage an OLAP cube and perform BI tasks, Microsoft created a query language, called multidimensional expressions (MDX), in the late 1990s. Many multidimensional database vendors have adopted MDX for data querying, but with this particular language, managing a cube requires staff with this specific skill set. Maybe an example can help you understand an OLAP cube a little better – it did for me.
OLAP cubes were initially explained to me as compared to a loaf of bread. So, follow me: if you think about a loaf of bread, the whole thing totaling 1,000 calories, each slice at 50 calories. The slices represent dimensions. If you then cut these slices into little pieces to make, say stuffing or croutons, each of these little chunks are about 5 calories and another dimension. If you take this comparison and apply it to an element of your business, like sales, the whole loaf (or OLAP cube) could be all of your sales from the very first one, the slices (or dimensions) representing years, and the croutons (more dimensions) might represent a region. All of this information is ordered and housed, and you can just pull the year or region that you need, and the database delivers it for you. Once configured, OLAP cubes can be considered self-service BI, with one main criterion.
To continue learning more about OLAP cubes, read the rest of this article