What's a MOLAP Tool?


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  Step 1:  The Concept

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ROLAP stands for Relational On-line Analytical Processing. It is a type of reporting tool that provides users with the capability to do efficient "drill" or "trend" analysis on large volumes of atomic or summarized data stored in a relational database. MOLAP stands for Multi-dimensional On-line Analytical Processing. It is a type of reporting tool that provides users with the capability to do efficient "drill" or "trend" analysis on summarized data stored in a multi-dimensional database.
  Step 2:  Drill Analysis

Data may have a hierarchical relationship such as year, month, and day. ROLAP and MOLAP tools provide users the same capabilities to analyze FACT variations at different levels of hierarchy. There are different types of drill analysis such as drill-down (using lower level attribute), drill-up (using higher level attribute), drill-within (using attribute from same dimension not in main hierarchy), drill-anywhere (using attributes from any dimension), and drill-across (using attributes from other dimensions).
  Step 3:  Trend Analysis

Data warehouses or data mart(s) contain historical data. ROLAP and MOLAP tools provide users the same capabilities to analyze FACT trends for different business perspectives (dimensions), having different levels of hierarchy, over a given time duration.
  Step 4:  Rotation Analysis

ROLAP and MOLAP tools may present data in similar simple tabular reports (along X-axis and Y-axis) such as year 2000 sales (in dollars) (intersection of X-axis and Y-axis) of sales person(s) (listed along Y-axis) for 12 months (listed along X-axis). Both tools provide users the same capabilities to perform rotation analysis of tabular data by interchanging data between X-axis and Y-axis such as year 2000 sales (in dollars) of sales person(s) (listed along X-axis) for 12 months (listed along Y-axis).
  Step 5:  Data Storage

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MOLAP tools may be used in analyzing data stored as a multi-dimensional data model (star or snowflake schema) in a multi-dimensional database (MDDB). Data is stored in denormalized dimensions and FACT(s) as long multi-dimensional arrays. Storage requirements may grow tremendously for storing summarized data in a MDDB when compared with a RDDB. New aggregated multi-dimensional data has to be pre-calculated and stored in a MDDB. MDDB examples are Oracle Express Server and Arbor's Essbase.
  Step 6:  Data Access

Data is pre-calculated and stored in a summarized format in a MDDB. A query will take a longer to time to read atomic data, summarize (or aggregate) it, and present it to users in a particular format when compared to a query that reads summarized data, and presents it to users in a particular format. Hence, query performance is predictable using a MOLAP tool. Also, it is necessary to store pre-calculated aggregate data in a MDDB.