If you do a quick comparison of today’s big data vendors, everyone looks pretty similar on the surface. Especially if you check the glossy brochures and promotional Web sites, you’ll find that everyone from small BI products to large database vendors are proudly showcasing:
- In-memory capabilities
- User-friendly interfaces
- Rich data visualization
- Robust analytics
How does SAS differ? The way we handle in-memory and analytics is different – and it’s not a small difference.
Everyone else is providing business intelligence really fast with a nice interface. But they’re not providing advanced analytics really fast, such as price optimization, predictive modeling, forecasting and statistical analysis.
Why? Because the structures of in-memory databases that rely on a strict SQL environment limit the types of analysis you can pull out of the system.
Everyone in the market right now is offering a system built on an in-memory database. Only one vendor is providing an in-memory analytic server.
So, what is that? It is an environment optimized for analytic processing. It is an environment that can deal with large amounts of data and the multiple passes necessary to take on advanced analytics algorithms.
These are the features that others don’t have. The difference between an in-memory database and an in-memory analytic server is the difference between the top and bottom half of the grid I showed when talking about big data BI vs big data analytics.
Without the in-memory analytic server, you’ll be unable to support proactive analytics because your analytics are limited by the structure of the database holding your data. You’ll still get fast answers in a pretty format, but the answers will be limited to the past. If you want fast answers that look into the future, there’s really only one choice.