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OK so everyone’s into big data but they’re usually talking about persistence, disk or more recently SSD, how about memory? We could simply add a few terabytes of RAM but even at $100 per GB that’s going to cost a LOT. What if we could reduce the size of the data by 50 fold and effectively bring the cost RAM down towards cost of disk? Keep Spring Integration, Spring Batch, GemFire in-memory cache, RabbitMQ messaging but reduce your data down to binary, yes bits and bytes rather than objects. Less garbage, less network overhead, same APIs but big-data in memory. John will show a Spring work-flow consuming 7.4kB XML messages, binding them to 25kB Java but storing them in just 450 bytes each, 10 million derivative contracts in-memory on a laptop.
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Big Data in Memory
John Davies is co-founder and CTO of Incept5. Incept5 have been intimately involved in implementing Visa's new capabilities and initiatives around the payments world. John's past includes global chief architect at JP Morgan and BNP Paribas, co-founder and CTO of C24 later sold to Iona and then Progress Software where he was technical director.