In-memory data grids see 2013 as a big year
January 17, 2013 —
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Another reason for the growth of in-memory data grids, said Allen, is the new focus on analytics in business. With Terracotta, or any other in-memory data grid, analytics can be run in real time because all the information is stored in RAM.
“If I'm doing transactions on an e-commerce site, I don't typically have a view into those transactions until after,” he said. “But I can now look at that data in-flight, and do real-time promotions or modify pricing. I can offer people incentives, or correlate current actual behavior with profile info about historical access.”
Uri Cohen, vice president of product management at GigaSpaces, said there are two major reasons for the growth of in-memory data grids. “The first is that the market for such solutions has definitely grown, with drivers such as the explosion of user-generated data and Web-scale deployments,” he said. “Whereas in the past most of the demand for these technologies came from high-end financial services and telecom applications, today it's prevalent in almost every vertical.
“We're seeing this demand in e-commerce, travel, fraud detection, homeland security, and SaaS implementations, to name a few. Some apps need to process tens or even hundreds of thousands of events per second, which is only feasible if you're using a distributed architecture. The in-memory aspect of things is what allows you to do it at real-time latencies, meaning you can do this as the events are flowing into your system and not have to wait for a batch Map/Reduce job to get the processed data and insights.
“The second trend is that with the advent of NoSQL data stores and cloud technologies, which drive people toward distributed architectures, the market is much better educated about such technologies and understands the trade-offs associated with them, with terms like CAP and BASE being widely known and reasonably well-comprehended. This saves us a lot of work in explaining our technology and how to implement your applications on top of it.”
Indeed, analytics are a major new draw to in-memory data grids, said Pezzini. “In the context of big data applications, what happens is the customers store data in memory to an in-memory data grid. In some cases, they are storing terabytes of data, and they want to run analytical types of applications on top of that. [That means supporting] query languages and Map/Reduce APIs. I believe this will be the next battleground.”