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Panel: How Banks Are Managing Their Data

Posted by Frank Tarsillo , John Davies, Jon Vernon and Ari Zilka  on  Aug 16, 2012

Frank Tarsillo , John Davies, Jon Vernon and Ari Zilka (moderator) discuss the technologies and architectures used these days to manage large amounts of sensitive data in top financial institutions.

Acceleration in the Wild with Dataflow Computing

Posted by James Spooner  on  Jul 24, 2012

James Spooner discusses the need to make good use of the underlying silicon using Dataflow computing and parallelism to improve throughput and latency for optimized data processing performance.

Modeling in a Cold Climate

Posted by Andrew Stewart  on  Jul 10, 2012 3

Andrew Stewart investigates the causes for so many bad models, especially in the financial sector, created by various teams including Agile ones.

AMQP in Financial Service

Posted by Hanno Klein  on  Mar 05, 2012

Hanno Klein explains how AMQP is used by Deutsche Börse and where it fits within their strategy.

Large Scale Integration in Financial Services

Posted by John Davies  on  Apr 26, 2011 4

John Davies addresses some of the difficulties dealing with FIX, FpML, SWIFT and integration in financial services software industry, challenging some of the canonical models existing today.

Groovy: Best Practices Developed From Distributed Polyglot Programming

Posted by Jonathan Felch  on  Jul 07, 2010 2

Jonathan Felch discusses Groovy, its major features, using it in a financial project, the benefit of using dynamic and meta-programming features together, ending with what is not so great in Groovy.

Pouring Data on Troubled Markets - Quantitative Portfolio Management Technology at BGI

Posted by Eoin Woods  on  Nov 05, 2009

Eoin Woods explains how Barclays Global Investors (BGI) designed Apex to meet the challenges it faces and the Java technologies which were chosen for an architecture with variations on standard J2EE.

Unshackle Your Domain

Posted by Greg Young  on  Jun 24, 2009 12

Our application runs over 10,000 sustained transactions per second with a rich model. The key? Modeling state transitions explicitly.

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