What Is Relational Online Analytical Processing (ROLAP)?
In the world of analytics, there are many ways to think about data. You can look at it in one, two, or maybe even three dimensions. You can analyze it by taking a bird's-eye view of all the data, or you can take a more granular approach and narrow your focus to just one piece of data at a time. What if we told you there was another way? What if we told you there was an entirely new way to look at your data? Introducing relational online analytical processing (ROLAP). This kind of online analytical processing (OLAP) analyzes data using multidimensional data models. Still, it does so by accessing data stored in a relational database rather than from a multifaceted database like most other OLAPs do. It can also generate SQL queries to perform calculations when an end-user wishes to do so. ROLAP can handle large volumes of data. Although it requires a lot of processing time to use in a relational database, all SQL tools can access it and it is not necessarily an OLAP-specific tool. Compared to multidimensional online analytical processing (MOLAP), ROLAP tools are better at processing non-aggregated facts such as textual descriptions. The most significant disadvantage of ROLAP is its performance. A ROLAP depends on the data being processed and can be slow if the data is large but fast if otherwise. Due to the fact that SQL statements do not always meet users' needs, especially when performing complex calculations, this technology also has limitations for SQL tools. The main drawback of ROLAP is that it is not very flexible compared to MOLAP, which allows for more flexibility when defining the data model. However, using cutting-edge methods like materialized views and snapshots to improve performance and decrease the amount of administration necessary can overcome this.
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