Historically, every computer has two types of data storage mechanisms – disk (hard disk) and RAM (Random Access Memory). Modern computers have more available disk storage than RAM but reading data from the disk is much slower (possibly hundreds of times) when compared to reading the same data from RAM. Especially when analyzing large volumes of data, performance is severely degraded. Using traditional disk based technology the query accesses information from multiple tables stored on a server’s hard disk. Traditional disk based technologies means Relational Database Management Systems such as SQL Server, MySQL, Oracle and many others. RDMS are designed keeping transactional processing in mind. Having a database that supports both insertions, updates as well as performing aggregations, joins (typical in BI solutions) is not possible. Also the structured query language (SQL) is designed to efficiently fetch rows of data while BI queries usually involve fetching of partial rows of data involving heavy calculations.
Though SQL is a very powerful tool running complex queries took very long time to execute and often resulted in bringing down transactional processing. To improve query performance multidimensional databases or cubes also called multidimensional online analytical processing (MOLAP) were formed. Designing a cube design involved an elaborate and lengthy process which took a significant amount of time from IT staff. Changing the cubes structure to adapt to dynamically changing business needs was cumbersome. Cubes are pre populated with data to answer specific queries and although it increased performance it still failed to answer ad hoc queries.[2]