SiSense Prism vs. In-Memory BI
Business Intelligence Software Comparison

What is In-Memory BI?

In-Memory Business Intelligence (BI) refers to business intelligence software that utilizes an in-memory database (IMDB) for data processing. In-Memory BI's claim to fame is to provide an alternative to the hefty data warehouse and OLAP projects.

An IMDB is a database management system (DBMS) that is designed for best performance when there is enough computer memory (RAM) to hold the needed data. This is in contrast to Relational Database Management Systems (RDBMS), for example, which are designed for best performance when the data cannot fit entirely in memory and (slow!) disk I/O operations must take place in real time.

Why All the Hype Over In-Memory BI?

In-memory databases have been around for 30 years, but they have only been catching headlines in the Business Intelligence space for the past few years. The main reason in-memory BI gained popularity recently is because it wasn’t feasible before 64-bit computing became commonly available. Before 64-bit processors, the maximum amount of RAM a computer could utilize was barely 4GB, which is hardly enough to accommodate even the simplest of multi-user BI solutions. Only when 64-bit systems became cheap enough did it became possible to consider in-memory technology as a practical option for BI.

In-Memory, or Out-Of-Memory BI?

However - unlike hard disk-based database solutions, for which it is easy to continuously add more storage at low cost, memory-based solutions require more and more relatively expensive memory to grow. While 64-bit PCs theoretically provide a very high maximum memory threshold, in practicality, deploying the required volumes of memory becomes prohibitive.This is because, with the in-memory approach, the entire data set must be loaded into memory at once.

When the size of the data (after compression) exceeds the amount of RAM, in-memory BI solutions become unusable (or even crash). Of course, as data volumes grow, so does the amount of memory required to hold it all at once. Companies with rapidly-growing data volumes will find that in-memory solutions will soon reach limits which make them impractical. This becomes substantially more obvious when there are multiple users accessing the same in-memory store.

ElastiCube - As Fast As In-Memory Databases, But Scalable

ElastiCube technology works differently. It was designed based on a thorough analysis of the strengths and weaknesses of both OLAP and in-memory technologies, while considering the off-the-shelf hardware available today (and tomorrow). The main benefit of ElastiCube is that it provides powerful OLAP-like functionality and scalable ad-hoc analytics - without the hefty projects and without compromising on the rapid implementation and fast query response times characterized by in-memory solutions.

Our Prism business intelligence software implements ElastiCube technology in a low-cost, easy-to-use package, with:

• Unprecedented data volume and concurrent user scalability, on commodity hardware
• Natively supported shared data scenarios (aka single version of the truth)
• High-speed query performance, without requiring OLAP cubes or pre-aggregations
• Ability to incorporate additional/changed data, w/o rebuilding the entire data model
• Support for a dimensional model and multidimensional analysis
• Separation between the BI application layer and the physical data layer
• SQL layer available to conform to industry standards

See Prism in Action - On a Very Large Database

To see a real life example of how ElastiCube technology handles ad-hoc analytics of a large database (on a $1200 PC), 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.