Adopting Big Data for Finance

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This article is the fourth in an editorial series that has the goal to provide direction for enterprise thought leaders on ways of leveraging big data technologies in support of analytics proficiencies designed to work more independently and effectively in today’s climate of working to increase the value of corporate data assets.

European Commission to Invest €2.5 Billion in Big Data

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“In this talk we summarize the results of the BIG project including analysis of foundational Big Data research technologies, technology and strategy roadmaps to enable business to understand the potential of Big Data technologies across different sectors, together with the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations.”

Performance Optimization of Hadoop Using InfiniBand RDMA

DK Panda

“The Hadoop framework has become the most popular open-source solution for Big Data processing. Traditionally, Hadoop communication calls are implemented over sockets and do not deliver best performance on modern clusters with high-performance interconnects. This talk will examine opportunities and challenges in optimizing performance of Hadoop with Remote DMA (RDMA) support, as available with InfiniBand, RoCE (RDMA over Converged Enhanced Ethernet) and other modern interconnects.”

New Survey from GE and Accenture Finds Growing Urgency for Big Data Analytics

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A new global study, “Industrial Internet Insights for 2015,” from GE (NYSE: GE) and Accenture (NYSE:ACN) reveals there is a growing urgency for organizations to embrace big data analytics to advance their Industrial Internet strategy.

LexisNexis Launches HPCC Systems® Developer Contest

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LexisNexis® Risk Solutions has announced its inaugural HPCC Systems Developer Contest. Developers and other technical professionals have the opportunity to demonstrate how they leveraged HPCC Systems to solve either a Big Data or Complex Query problem.

Types of In-Memory Computing

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In this installment we’ll set the stage for in-memory computing technology in terms of its current state as well as its next stage of evolution. We’ll begin with a discussion of the capabilities of in-memory databases (IMDBs) and in-memory data grids (IMDGs), and show how they differ. We’ll finish up the section by demonstrating how neither one is sufficient for a company’s strategic move to IMC; instead, we will explain why a comprehensive in-memory data platform is needed.

Predictive Modeling and Production Deployment

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Using predictive analytics involves understanding and preparing the data, defining the predictive model, and following the predictive process. Predictive models can assume many shapes and sizes, depending on their complexity and the application for which they are designed. The first step is to understand what questions you are trying to answer for your organization.

Spark Panel Discussion with Cloudera, MapR & Pivotal

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The panel discussion video below comes from the Los Angeles Spark Users Group. The talk fosters a lively discussion on Spark’s initial goals, where it came from and what the future holds for Spark. Many leading Big Data vendors are responding by introducing Spark’s capabilities into their architectures. The panel discussion is between the top Hadoop distribution vendors – Cloudera, MapR, and Pivotal.

Credit Scoring and Back Trading/Testing

Guide to Big Data Finance - Thumbnail

This article is the third in an editorial series that has the goal to provide direction for enterprise thought leaders on ways of leveraging big data technologies in support of analytics proficiencies designed to work more independently and effectively in today’s climate of working to increase the value of corporate data assets.

MongoDB Named NoSQL Leader by Forrester Research

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MongoDB has announced it was named a Leader by Forrester Research Inc. in the report, “The Forrester Wave™: NoSQL Document Databases, Q3 2014.” Forrester evaluated 57 criteria including performance, scalability, security, integration, and high availability.