Relational OLAP
Relational online analytical processing (ROLAP) provides OLAP functionality by using relational databases and familiar relational query tools to store and analyze multidimensional data. That approach builds on existing relational technologies and represents a natural extension to all of the companies that already use relational database management systems within their organizations. ROLAP adds the following extensions to traditional RDBMS technology:
• Multidimensional data schema support within the RDBMS.
• Data access language and query performance optimized for multidimensional data.
• Support for very large databases (VLDBs).
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OLAP and Its Characteristics
I. Multidimensional Data Schema Support within the RDBMS:
Relational technology uses normalized tables to store data. Normalization divides business entities into smaller pieces to produce the normalized tables. For example, sales data components might be stored in four or five different tables. The reason for using normalized tables is to reduce redundancies, thereby eliminating data anomalies, and to facilitate data updates. Unfortunately, for decision support purposes, it is easier to understand data when they are seen with respect to other data.
ROLAP uses a special design technique to enable RDBMS technology to support multidimensional data representations by using special design technique is known as a star schema. Naturally, changing the data design foundation means that the tools used to access such data will have to change. End users who are familiar with the traditional relational query tools will discover that those tools do not work efficiently with the new star schema. However, ROLAP saves the day by adding support for the star schema when familiar query tools are used. ROLAP provides advanced data analysis functions and improves query optimization and data visualization methods.
II. Data Access Language and Query Performance Optimized for Multidimensional Data:
Another criticism of relational databases is that SQL is not suited for performing advanced data analysis. Most decision support data requests require the use of multiple-pass SQL queries or multiple-nested SQL statements. To answer this criticism, ROLAP extends SQL so that it can differentiate between access requirements for data warehouse data (based on the star schema) and operational data (normalized tables). In that way, a ROLAP system is able to generate the SQL code required to access the star schema data.
Query performance is also improved because the query optimizer is modified to identify the SQL code’s intended query targets. However, if the end user performs drill-down queries against operational data, the query optimizer identifies that operation and properly optimizes the SQL requests before passing them through to the operational DBMS.
Another source of improved query performance is the use of advanced indexing techniques such as bitmapped indexes within relational databases.
III. Support for Very Large Databases:
Recall that support for VLDBs is a requirement for decision support databases. Therefore, when the relational database is used in a decision support role, it also must be able to store very large amounts of data. Both the storage capability and the process of loading data into the database are crucial. Therefore, the RDBMS must have the proper tools to import, integrate, and populate the data warehouse with data. Decision support data are normally loaded in bulk (batch) mode from the operational data. However, batch operations require that both the source and the destination databases be reserved (locked). With open client/server architecture, ROLAP provides advanced decision support capabilities that are scalable to the entire enterprise. Clearly, ROLAP is a logical choice for companies that already use relational databases for their operational data.
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