In-Memory BI Checklist

What To Look For From Your
In-Memory BI Platform

Traditional disk-based BI tool providers have increasingly announced that they have added in-memory architectures to their tools. QlikView is the only solution architected from the beginning to be a pure in-memory analytic solution.

Top-to-Bottom In-Memory

QlikView does not require data to be staged or stored into any disk-based database prior to being used by end users. The problem with disk-based solution is the disk-based cubes and warehouses – they are costly and time consuming to set up and maintain. Just layering a new in-memory system on top does nothing to correct the underlining technical limitations.

Complete Solution

QlikView provides a complete solution allowing users to consolidate data, search associatively, and calculate and visually present analysis. Other in-memory solutions only provide a part of the solution and either exclude important capabilities or rely on prior generation technology to fill their gaps. Some provide beautiful in-memory charts, but no data consolidation or core calculation capability of their own and no end user tools for visualizing or interacting with their product.

Standard Hardware

QlikView runs on standard, off-the-shelf, readily available hardware from a variety of providers. It takes advantage of next generation standard hardware capabilities such as 64-bit addressing for large memory spaces, and multi-core processors for high performance parallel calculation. Other in-memory solutions require proprietary hardware solutions and even proprietary operating systems that are costly to buy and maintain because they require specialized skills that can only be found with the original vendor.

Leverage Speed of Memory

By leveraging the speed of memory, QlikView offers new capabilities not possible in traditional disk-based solutions, which attempt to simply place their old functionality in memory. With QlikView, end users can: create new analysis because there is no danger of creating a "bad query", understand associative relationships (or the lack of) within their entire data set, collaborate to share insights, change analysis at will by building on real-time calculations and conduct new types of analytics such as aggregations of aggregations and set analysis which are impossible or too time consuming on disk and much more.