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Streaming Analytics Ready for Prime Time, Forrester Says

Jul 22, 2014 |

Analytic platforms that generate insights from data in real time are mature enough for enterprises to begin adopting them, Forrester says in its latest report. While open source streaming analytic products like Apache Storm are proving popular, Forrester says they lack key functionality found in the offerings of proprietary vendors, such as top-rated Software AG. You don’t need a Forrester analyst to know that streaming analytics is red hot at the moment. If Hadoop has opened our eyes to what Read more…

In-Memory Computing Is the Key to Real-Time Analytics

Feb 17, 2014 |

Real-time analytics offers enterprises the ability to examine “live,” fast-changing data within operational systems and obtain feedback in milliseconds to seconds. For example, a hedge fund in a financial services organization can track the effect of market fluctuations on its portfolios (“strategies”) of long and short equity positions in various market areas (high tech, real estate, etc.) and immediately identify strategies requiring rebalancing.

Accelerating Hadoop MapReduce Using an In-Memory Data Grid

Jan 6, 2014 |

Hadoop MapReduce has been widely embraced for analyzing large, static data sets. New technology integrates a stand-alone MapReduce engine into an in-memory data grid, enabling real-time analytics on live, operational data. This dramatically shortens analysis time by 20x from minutes to seconds. Numerous applications now can benefit from real-time MapReduce.

How In-Memory Data Grids Can Analyze Fast-Changing Data in Real-Time

Nov 18, 2013 |

The ability to continuously analyze operational data unlocks the potential for organizations to extract important patterns. Popular big data systems are not well suited for this challenge. However, in-memory data grid technology (IMDGs) offers important breakthroughs that enable real-time analysis of operational data. Benchmarks have demonstrated that an IMDG can complete map/reduce analyses every four seconds across a changing, terabyte data set. This article discusses how IMDGs deliver this new capability to analyze fast-changing, operational data.

Accelerating Hadoop MapReduce Using an In-Memory Data Grid

Oct 21, 2013 |

Hadoop MapReduce has been widely embraced for analyzing large, static data sets. New technology integrates a stand-alone MapReduce engine into an in-memory data grid, enabling real-time analytics on live, operational data. This dramatically shortens analysis time by 20x from minutes to seconds. Numerous applications now can benefit from real-time MapReduce.

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