The typical architecture of an OLAP solution resembles the diagram shown below.

Data is integrated by loading it into a centralized database called a Data Warehouse. The data warehouse holds data from all sources and is usually clean and ready for business use.

However, most business users do not have direct access to the data warehouse but rather to OLAP cubes that are built over it.

Why OLAP

OLAP cubes have two main purposes. The first is to provide business users with a data model more intuitive to them than a tabular model. This model is called a Dimensional Model.

The second purpose is to enable fast query response that is usually difficult to achieve using tabular models.

How OLAP Works

Fundamentally, OLAP has a very simple concept. It pre-calculates most of the queries that are typically very hard to execute over tabular databases, namely – aggregation, joining and grouping. These queries are calculated during a process that is usually called ‘building’ or ‘processing’ of the OLAP cube. This process happens overnight, and by the time end users get to work - data will have been updated.

Why OLAP Is Becoming Obsolete

OLAP technology started gaining popularity in the late 1990s, and that had a lot to do with Microsoft’s first release of their OLAP Services product (now Analysis Services), based on technology acquired from Panorama Software. At that point in time, computer hardware wasn’t nearly as powerful as it is today; given the circumstances at the time, OLAP was groundbreaking. It introduced a spectacular way for business users (typically analysts) to easily perform multidimensional analysis of large volumes of business data.

Many things have changed since the 90s and after many years of OLAP implementations it became quite understood that pre-calculating and pre-aggregating data has significant drawbacks. Here are just a few:

• Restructuring the data warehouse into the star schema / snowflake schema OLAP requires is exhaustive work that requires significant technical expertise.
• Pre-aggregating data means business users lose simplified access to details inherent in the raw data. This is unacceptable to most modern business operations.
• OLAP cubes tend to exponentially grow in size, compared to the original data.
• OLAP cubes are very rigid and are limited in terms of the number of dimensions they consist of, which means separate OLAP cubes must be created for different business scenarios (making it hard to cross-reference data).

ElastiCubes - Evolving Beyond the OLAP Cube

ElastiCube technology was officially introduced to the market in late 2009, after more than five years of research and development conducted in complete secrecy. After being proved practical and effective in the real world (by being successfully implemented at over 100 companies, paying customers in numerous industries, from startups to multinational corporations), SiSense proceeded to expand awareness of the Prism Business Intelligence product which is based on the technology.

ElastiCube is the result of thoroughly analyzing the strengths and weaknesses of both OLAP and in-memory technologies, while taking into consideration the off-the-shelf hardware of today and tomorrow. The vision was to provide a true alternative to OLAP technology, without compromising on the speediness of the development cycle and query response times for which in-memory technologies are lauded. This would allow a single technology to be used in BI solutions of any scale, in any industry.

Below is the typical architecture for an ElastiCube solution. You will notice that it is significantly simplified:

Learn More About ElastiCube and OLAP

See Prism in Action - On a Very Large Database (Without Pre-Aggregations)

To see a real life example of how ElastiCube technology handles ad-hoc analytics of a large database, watch the following video. This video shows a business user using SiSense Prism to build a report over a very large operational database containing 13 tables, the largest of which hold 100 million and 40 million rows. While databases of this size were once rare - now, any company who has a properly tracked website quickly accumulates very large data sets. The computer holding the dataset is a $1200 off-the-shelf PC with 6GB of RAM, 100GB of disk space and a single quad-core CPU (64-bit).

Learn More About SiSense Prism