Whitepapers


The Rise of the Analytic Platform in Big DataOffloading Analytic Workloads for Improved Performance

A Leader’s Guide to the Need and Requirements for Analytic Offload

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Financial Services

Accelerate Insight for Financial Services: Manage Risk. Stop Fraud. Increase Profits.

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ParAccel Analytic Platform

A Technical Overview

Rapidly accumulating business intelligence data presents an amazing opportunity for all types of corporations. But “big data” can be overwhelming—and analyst firm IDC predicts this data will multiply by 44 times over the next decade, so it won’t get any easier to make sense of.

Financial institutions in particular need high-performance analytic databases more than ever. Rapidly changing markets, new regulations, new investment options, and new market opportunities must be constantly and rapidly analyzed for new insights with risk analysis, which relies on terabytes and petabytes of historical data. This requires the superior performance delivered by today’s analytic databases.

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The Rise of the Analytic Platform in Big DataThe Rise of the Analytic Platform in Big Data

By Enterprise Management Associates®
 

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Creating the Analytics-Driven Enterprise

New World, New Analytics

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Analytic Platform Selection

Criteria for Analytic
Platform Selection

ParAccel has continued its attempts to differentiate itself in a crowded market by focusing its analytic-database products on high-performance analytics workloads. With version 3.0 of the ParAccel Analytic Database (PADB), the company has added in-database analytics capabilities that are due to be extended over the next year.

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TPC Benchmark H -
Full Disclosure Report

VMware® ESX™ Using
ParAccel Analytic Database™

This report examines the rise of “big data” and the use of analytics to mine that data. Companies have been storing and analyzing large volumes of data since the advent of the data warehousing movement in the early 1990s. While terabytes used to be synonymous with big data warehouses, now it’s petabytes, and the rate of growth in data volumes continues to escalate as organizations seek to store and analyze greater levels of transaction details, as well as Web- and machine-generated data, to gain a better understanding of customer behavior and drivers.

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