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SpringOne2GX 2014 Replay: Big Data in Memory

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SpringOne2GX 2014 Replay: Big Data in Memory

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In the complimentary O’Reilly eBook, Migrating to Cloud-Native Application Architectures, Pivotal’s Matt Stine examines the cultural, organizational, and technical changes necessary to migrate traditional monolithic applications and service-oriented architectures to cloud-native architectures.

Recorded at SpringOne2GX 2014. Big Data in Memory

Speaker: John Davies, C24

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014-john-davies-41130273

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.

Learn more about Spring XD: http://projects.spring.io/spring-xd

Learn more about Spring Integration: http://projects.spring.io/spring-integration

Learn more about Spring Batch: http://projects.spring.io/spring-batch

In this complimentary eBook, you will find a migration cookbook, with recipes for decomposing monolithic applications into microservices, implementing fault-tolerant patterns, and performing automated testing of cloud-native services.

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Published at DZone with permission of Pieter Humphrey, DZone MVB. See the original article here.

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