Versant Throws Magic Cube at Big Data

Recent advances in technology coupled with a massive influx of users have all but rendered the classic SQL database model inefficient for today’s HPC and consumer needs.

Ultimately, getting control of big data along with actionable analysis seems to be the solution for maximizing revenues and productivity in today’s evolving markets. One company that straddles the high performance computing and commercial markets plans to make that job easier with a little object-oriented magic.

There is simply too much data growth and real-time requests for a single database server to handle on it’s own. Enter “big data”, the current Rubik’s cube in advanced technology today. One company that has been around to see these changes in exponential data growth is Versant and it believes it has the answer to the puzzle.

Versant began in 1988 with an object-based database and has provided solutions to Verizon, British Airways, General Electric, and NASA. When it comes to their definition of “big data” Versant claims “…when the data volume and performance requirements become significant design and decision factors for implementing a data management and analysis system” and with Gartner  “…. [when]volume, complexity, speed, changing analysis model, are increasingly becoming important design factors in enterprise architectures” .. So the current need for scalable, highly available and high performing data has grown the need for a new type of data solution.

In a case study, Versant was tapped by Northrop Grumman to create a new version of the Cloud Depiction and Forecast System (CDFS II) for the US Air Force that could deliver data 6 times faster with 4 times the resolution compared to their current simulator (CDFS I).

To achieve this goal, as with most big data applications, massive amounts of information are collected from sensors, nine satellites, and a feed from the Air Force Global Theatre Weather Analysis and Prediction System. Coupling this data with complex object oriented algorithms allowed for much lower overhead as compared to a relational database model, allowing CDFS II the ability to produce forecasts 12 hours ahead for the entire globe every hour and a sixty-hour forecast every 6 hours…this is compared to CDFS I which was only able to produce a 48-hour forecast every six hours with ¼ of the resolution.

Since each big data scenario has different unique needs, Versant has introduced the Magic Cube, which is focused on delivering an application that is meant to solve a company’s current and future big data problems while keeping an appropriately sized footprint. The Magic Cube creates solutions based on 3 main areas of data performance: volume, concurrency and complexity.

Basically, if you have massive amounts of sensor data, but do not require over a thousand queries per second, you can ask for more volume without investing in massive concurrency, However, if you are a social network, you would probably need a robust solution for volume, concurrency and complexity.

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