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Tag: scaleout

Accelerating Hadoop MapReduce Using an In-Memory Data Grid

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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. Read more…

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

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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. Read more…

Accelerating Hadoop MapReduce Using an In-Memory Data Grid

Sponsored Content

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. Read more…

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

Sponsored Content

Continuous analysis of fast-changing operational data unlocks the potential to extract important patterns. Big data systems such as Hadoop are not well suited for this challenge, however, in-memory data grids (IMDGs) offer breakthroughs that enable real-time analysis of fast-changing data. Recent measurements demonstrate that an IMDG can deliver a complete map/reduce analysis every four seconds across a terabyte data set which is being updated continuously.<br /> Read more…

Using In-Memory Data Grids for Global Data Integration

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By enabling extremely fast and scalable data access even under large and growing workloads, in-memory data grids (IMDGs) have proven their value in storing fast-changing application data, such as financial trading data, shopping data, and much more. As organizations work to efficiently access their critical business data across multiple sites or scale their processing into the cloud, the need rapidly has grown to quickly and seamlessly migrate data where it is needed. The use of IMDGs creates an exciting opportunity for organizations to employ powerful global strategies for data sharing. Federating IMDGs across multiple sites enables seamless, transparent access to data from any site and provides an ideal solution to the challenge of global data integration. Read more…

Ephemeral, Fast Data Finds Home in Memory

For any number of businesses, big data isn’t useful unless it’s fast and responsive data. In other words, the challenges that reflect the volume and complexity sides of the big data coin represent just one face. To discuss this and some new approaches that are changing in memory data grid usage, we speak with Dr. William Bain of ScaleOut.... Read more…

Using In-Memory Computing to Simplify Big Data Analytics

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The “big data” revolution is upon us, fed by the need in both the public and private sectors to quickly analyze large datasets for important patterns and trends. With big data analysis, ecommerce vendors can target customers more precisely, financial analysts can quickly spot changing market conditions, manufacturers can tune logistics planning, and the list goes on. They all need powerful, easy to use analysis tools to maintain a competitive edge. Read more…

Using an In-Memory Data Grid for Near Real-Time Data Analysis

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With the ever increasing explosion in data for analysis and the need for fast insights on emerging trends, in-memory data grids (IMDGs) offer a highly attractive platform for hosting map/reduce analysis. In comparison to disk-based map/reduce platforms such as Hadoop, IMDGs reduce analysis times by reducing data motion while simplifying the development model. For applications which need to analyze fast-changing application data, such as shopping or financial trading data, IMDGs can provide near real-time results. Read more…

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