The two types of OLAP are MOLAP (Multidimensional OLAP) and ROLAP (Relational OLAP) as explained
1. MOLAP: In this type of OLAP, a cube is aggregated from the relational data source (data warehouse). When user generates a report request, the MOLAP tool can generate the create quickly because all data is already pre-aggregated within the cube.
2. ROLAP: In this type of OLAP, instead of pre-aggregating everything into a cube, the ROLAP engine essentially acts as a smart SQL generator. The ROLAP tool typically comes with a 'Designer' piece, where the data warehouse administrator can specify the relationship between the relational tables, as well as how dimensions, attributes, and hierarchies map to the underlying database tables.
Right now, there is a convergence between the traditional ROLAP and MOLAP vendors. ROLAP vendor recognize that users want their reports fast, so they are implementing MOLAP functionalities in their tools; MOLAP vendors recognize that many times it is necessary to drill down to the most detail level information, levels where the traditional cubes do not get to for performance and size reasons.
The criteria for evaluating OLAP tools:
- Ability to leverage parallelism supplied by RDBMS and hardware: This would greatly increase the tool's performance, and help loading the data into the cubes as quickly as possible.
- Performance: In addition to leveraging parallelism, the tool itself should be quick both in terms of loading the data into the cube and reading the data from the cube.
- Customization efforts: More and more, OLAP tools are used as an advanced reporting tool. This is because in many cases, especially for ROLAP implementations, OLAP tools often can be used as a reporting tool. In such cases, the ease of front-end customization becomes an important factor in the tool selection process.
- Security Features: Because OLAP tools are geared towards a number of users, making sure people see only what they are supposed to see is important. By and large, all established OLAP tools have a security layer that can interact with the common corporate login protocols. There are, however, cases where large corporations have developed their own user authentication mechanism and have a "single sign-on" policy. For these cases, having a seamless integration between the tool and the in-house authentication can require some work. I would recommend that you have the tool vendor team come in and make sure that the two are compatible.
- Metadata support: Because OLAP tools aggregates the data into the cube and sometimes serves as the front-end tool, it is essential that it works with the metadata strategy/tool you have selected.
- Business Objects
- IBM Cognos
- SQL Server Analysis Services
- Palo OLAP Server
Slicing means taking out the slice of a cube, given certain set of select dimension (product), and value (home furnishings..) and measures (sales value, sales units..). Dicing means viewing the slices from different angles. For example -Revenue for different products within a given state or revenue for different states for a given product. One form of Slicing and Dicing is called pivoting.
Slicing means taking out the slice of a cube, given certain set of select dimension (customer segment), and value (home furnishings..) and measures (sales revenue, sales units..) or KPIs (Sales Productivity). Dicing means viewing the slices from different angles. For example -Revenue for different products within a given state OR revenue for different states for a given product.
Slicing and Dicing leads to what you can call Pivot. Pivot is known in Excel context. Pivot is the standard and basic look and feel of the views you create on the OLAP cubes. A pivot creates an ability for you to create the width and depth in your view of the data.
A pivot is a two dimensional lay-out of the summary data. The x and y axis are the dimensions and the intersection cells for any two dimension values contain the value of the measures.
To slice and dice is to break a body of information down into smaller parts or to examine it from different viewpoints so that you can understand it better. In cooking, you can slice a vegetable or other food or you can dice it (which means to break it down into small cubes). One approach to dicing is to first slice and then cut the slices up into dices. In data analysis, the term generally implies a systematic reduction of a body of data into smaller parts or views that will yield more information. The term is also used to mean the presentation of information in a variety of different and useful ways.