Data Warehousing Olap Tool Selection

Data Warehousing > Tools > OLAP Tool Selection February 25, 2009 by Deepanshu Mehta Buy vs. Build OLAP tools are geared towards slicing and dicing of the data. As such, they require a strong metadata layer, as well as front-end flexibility. Those are typically difficult features for any home-built systems to achieve. Therefore, my recommendation is that if OLAP analysis is part of your charter for building a data warehouse, it is best to purchase an existing OLAP tool rather than creating one from scratch. OLAP Tool Functionalities Before we speak about OLAP tool selection criterion, we must first distinguish between the two types of OLAP tools, MOLAP (Multidimensional OLAP) and ROLAP (Relational OLAP). 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. So what are the criteria for evaluating OLAP vendors? Here they are: •

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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.

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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.

Popular Tools • • • • • • •

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