OLAP (Online Analytical Processing) was introduced into the business intelligence (BI) space over 20 years ago, in a time where computer hardware and software technology weren’t nearly as powerful as they are today. Back then, given the circumstances, OLAP was groundbreaking. It introduced a spectacular way for business users (typically analysts) to easily perform multidimensional analysis of large volumes of business data. In analytics, it is often required to aggregate, group and join data.

These operations represent the most difficult types of queries for a relational database to process. Therefore, the magic behind OLAP derives from its ability to pre-calculate and pre-aggregate data.


The ability to pre-calculate and pre-aggregate data is the fundamental enabler of OLAP-based BI solutions, otherwise end users would be spending most of their time waiting for query results to be returned by the database. However, it is also what causes OLAP-based solutions to be extremely rigid and very IT-intensive.

Here are a few reasons why:

  • OLAP requires restructuring of data into a star/snowflake schema – complicated!
  • There is a limited number of dimensions (fields) a single OLAP cube
  • It is nearly impossible to access transactional data in the OLAP cube
  • Changes to an OLAP cube requires a full update of the cube – lengthy process!
OLAP from Ashir Ali


Similar to OLAP-based solutions, Sisense is a Business Intelligence software designed to enable solutions where multiple business users perform ad-hoc data analysis on a centralized data repository. On the other hand, Sisense does not achieve this by pre-calculating query results, but rather by utilizing state-of-the-art technology called ElastiCube. It is a sophisticated columnar database, which was specifically designed for Business Intelligence solutions. Its unique storage and memory processing technology radically change the way business intelligence solutions access data.

Powered by ElastiCube, Sisense delivers distinct advantages over OLAP-based solutions:

  • Instant query response times, without pre-calculation or pre-aggregation of data
  • Creation of complicated star/snow flake schemas is not required
  • A data warehouse is not required, but easily supported
  • There are no physical limits to the number of dimensions an ElastiCube can hold
  • ElastiCube provides access to data in any granularity (not merely to aggregated data)
  • Changes to ElastiCubes can be done without re-building the entire data model
  • An ElastiCube requires significantly less powerful hardware than a similar OLAP cube

Further reading

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