Aggregations - The reason for popularity of OLAP servers

One of the major reasons for the popularity of OLAP servers has been their speed in resolving queries. Numerically speaking, they are capable of producing an answer to even the most complex queries in approximately 0.1% of the time as would be taken on OLTP relational data, which is basically the concept upon which OLAP has been developed, so it obviously makes sense for it to be faster than the other. But the main reason for such phenomenally high speed is the use of aggregations, that are built from the fact table following a change in the granularity on specific dimensions and aggregation of data along these very dimensions. The number of aggregations that are possible in each case depends on every possible combination of dimension granularities.

Further, since there are numerous aggregations that can be calculated on OLAP servers, it often happens that only a specific number of the same are fully calculated, with the rest being solved on demand. The problem of choosing which of these aggregations need to be calculated is commonly known as the 'View Selection' problem, which can be restricted by the size of the selected set of aggregations, time to update them from changes in the base data, and in some cases, both. The basic purpose of view selection is to reduce the average time to respond to OLAP queries, so as to further enhance the utility of OLAP servers for organizations.

OLAP servers – Your tool for better decision making

Decision making and business growth go hand in hand, since without one, the other is incomplete, to say the least. However, with the increasing dependence on technology to provide the solutions to everyday problems, irrespective of how complicated they might be, the margin of error is slowly reducing, since computers always provide the most accurate information. However, the problem is not of accuracy alone, but of speed as well. And this is where OLAP servers come into play.

OLAP, or Online Analytical Processing, is a methodology to deliver quick access to gigantic amounts of data. Multidimensional data representations, or cubes, are used by OLAP servers to provide such flawless performance, which is often more valuable to organizations than some of their top-notch employees. The reason for this is the kind of analysis that can be derived from the usage of OLAP. Decision makers in particular find this system indispensible, as they are always required to get fast access to numerous different statistics, reports, or information in any other format, and OLAP makes that happen. OLAP systems are basically available in two architectures – Relational OLAP (ROLAP) and Multidimensional OLAP (MOLAP). According to their requirements, organizations can implement either of the two to get faster and more accurate access and analysis of data.

What is MOLAP?

MOLAP, or multidimensional Online Analytical Processing, is one of the technologies used in OLAP servers, and is an alternative to ROLAP. Although both MOLAP and ROLAP analytic tools facilitate analysis of data via the usage of a multidimensional data model, MOLAP's point of difference comes with the fact that it requires pre-computation and storage of information in the cube (explained in the previous post), which is certainly an effort in the initial stages, but one that pays off handsomely later on. Following are some of the benefits of MOLAP over ROLAP:

• MOLAP cubes allow extremely quick data retrieval due to multidimensional indexing and caching, as well as optimized storage. They are also perfect for slicing and dicing operations.
• They are capable of complex calculations, since all of the same have been pre-generated during the creation of the cube. Also, while there is a certain disadvantage in the fact that the cube once created can't hold any more data, this does not mean that it can't store tremendous amounts of data. Hence, with a bit of future planning, MOLAP can serve the organization's interests quite well.
• There is a smaller amount of on-disk data as compared to ROLAP since there are some pretty advanced compression techniques used in MOLAP.
• There is automatic computation of higher level aggregates of data.
• Array models facilitate natural indexing.

So as you can see, there are actually many reasons why companies prefer MOLAP over ROLAP as the technology for their OLAP server.