ROLAP is short for Relational On-Line Analytical Processing. It is one of the major subsets of OLAP, along with MOLAP and HOLAP. Confused by all the acronyms? Let's look at just this for now.

The relational part of the name refers to the way the data is stored for the OLAP processing. The tool will use a database set up with relationships (also called RDBMS) as the source, which the OLAP tool then queries.

The advantages lay in the amount of data it can hold. It can really be as large as the underlying database. Realistically though, it normally wouldn’t be that large. Its theoretically unlimited, however machine constraints really put the limit at the amount of RAM available. MOLAP setups are known to have faster retrieval times.

It is ideal when there is a large database that is optimized for a relational modeling perspective. It’s also good for a scenario where summary or aggregate tables can be created to speed up data retrieval. This would generally be a data warehouse that has data modeling in a star schema or other relational model. The disadvantages are that the data, in most cases, needs to be modeled ahead of time to support the required OLAP analysis in an efficient way. It also tends to put more of the query load on the database itself, although some relational tools will perform some of the analytic calculations itself. Some might argue that this methodology is slow, but it really depends on the amount of data and modeling techniques that were used. While it provide a “slice and dice” view, the underlying SQL queries can be pushed performance wise, so those may need to be tuned.

Different BI vendors have embraced this to varying degrees in their tools. MicroStrategy has pushed the technology in its OLAP services, as it is optimized to run on a RDBMS system. Some, like Oracle Essbase, use more of the MOLAP model.

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