BI Strategy - Cost Effective On-The-Fly Data

Indeed the demands are continuously increasing and the operational costs are getting higher! With this, how could we keep up with the increasing demand, pay up the higher operational costs and still keep your feet steady on the ground?

Started in 2006, Truviso pioneered on the on-the-fly or continuous analysis of incoming data. Compared to the widely used real time data analysis the continuous method has zero latency even for massive volumes of data.

"With data growth rates significantly higher than the rate at which hardware is getting faster, Moore's law cannot keep up with the flood of data," Truviso co-founder and CTO Mike Franklin explains.

Problem in a nutshell

Franklin explained that there are actually three forces that needs to be reckoned with when it comes to analytics solution. Number one is the continuous surge in the amount of data that organizations needed. Quoting even Richard Winter, an expert in large database technology, typical data volumes increases at a rate of one-and-a-half to two-and-a-half times a year! Now how could we keep up with that pace?

The result of such influx of large volumes of data many companies are now on the losing end since they have to make their ends meet with the spiraling costs in terms of servers, people, power, cooling and space.

Also such growth compels more and more organization to quickly decide on the matter giving way to lousy decisions without thorough deliberation and analysis. According to Franklin, combining all of these would give us the perfect storm which is impossible for traditional data analytics method.

Solution in a snap

This was all solved by another business intelligence technique by Truviso. They have figured how to seamlessly integrate a high-performance stream processing engine inside a an SQL relational database system. The said company designed streams as tables that arrive on a continuous basis giving way for queries to be written over streams, over tables, or over any combination of the two.

Franklin said that this is what we call the "rip and replace" technology and this could ignite magnitude improvements in both scalability and latency enabling businesses to handle the increasingly demanding analytics workloads of today's data-intensive businesses.