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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.

Credit Scoring and Back Trading/Testing

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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.

The Business Case for In-Memory Computing

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This article is the second in an editorial series that will provide direction for enterprise thought leaders on ways of leveraging in-memory computing to analyze data faster, improve the quality of business decisions, and use the insight to increase customer satisfaction and sales performance.

Data Access and Exploratory Data Analysis

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Enterprise data assets are what feed the predictive analytic process, and any tool must facilitate easy integration with all the different types data sources required to answer critical business questions. Robust predictive analytics needs to access analytical and relational databases, OLAP cubes, flat files, and enterprise applications.

Visualization of the Week: US Weather Trends

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This week’s visualization choice was selected because it really hit home for me living in Los Angeles. Just in case you’re not familiar with our plight here in Hollyweird, we have a case of perpetual summer and severe drought conditions which when coupled together have produced a rather harsh climate in which to live.

Big Data and Retail Banking

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This article is the second 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.

insideBIGDATA Guide to In-Memory Computing

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In this new Guide to In-Memory Computing the goal is to provide direction for enterprise thought leaders on ways of leveraging in-memory computing to analyze data faster, improve the quality of business decisions, and use the insight to increase customer satisfaction and sales performance.

Predictive Analytics Software and R

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There is a vast array of predictive analytics tools, but not all are created equal. Software differs widely in terms of capability and usability — not all solutions can address all types of advanced analytics needs. There are different classes of analytics users — some need to build statistical models, others just need to use them.