The office supplies retailer say it sacrificed some sales to improve online profitability. It also redesigned its business-facing e-commerce site, StaplesAdvantage.com.
ith ScaleOut Analytics Server, developers can easily take advantage of powerful, in-memory analytics to quickly implement their big data applications and near real-time analytics performance.
BELLEVUE, Wash., October 2, 2012 - ScaleOut Software, a leading provider of in-memory data grids (IMDGs), today announced the immediate availability of ScaleOut Analytics Server™, an IMDG-based analytic computing platform that integrates map/reduce functionality and automated code shipping, enabling organizations to accelerate the development and deployment of analytic applications and achieve near real-time data analytics.
As business applications continue to generate overwhelming volumes of data, developers are tasked to make sense of it all. Popular open source approaches to big data analytics use complex stacks of software components that require specialized expertise to optimize performance. File IO and batch scheduling overheads create performance bottlenecks since data must be moved into memory before it can be processed. In contrast, ScaleOut Analytics Server uses the distributed RAM in a server cluster as a platform for the analysis applications, providing simple and familiar object-oriented programming techniques to drive in-memory computing operations with minimum data motion to achieve fast, scalable performance with near real-time responsiveness.
“Hadoop has created great interest in data analysis, catalyzed by disruptively inexpensive storage, and the massive processing power that exists in a cluster of modern servers,” said Peter Christy, co-founder and principal analyst, Internet Research Group. “But most enterprise applications are quite different from the problems addressed by Google and Yahoo. There are an emerging set of hybrid solutions that leverage Hadoop for storage and unstructured data analysis, but build quite different analytic solutions on top of that using the massive distributed RAM stores in the cluster as a key asset. IMDG’s are a great platform for large analysis problems, but tricky to implement well. Tools like the ScaleOut Analytics Server can play an important role in accelerating and improving real big data applications.”
ScaleOut Analytics Server's powerful analytic computing platform combines scalable and distributed in-memory data storage with a parallel computing engine to scale application performance and deliver near real-time data analysis. For example, developers can now use integrated map/reduce functionality to analyze fast-changing data, such as clickstream data for optimizing online promotions, stock trading data to implement trading strategies, or machine log data to tune manufacturing processes.
“As the need for big data analytics quickly proliferates and adoption continues to grow, the development effort and expertise required to make use of complex, open source analytics solutions has become an obstacle to adoption,” said Bill Bain, CEO, ScaleOut Software. “ScaleOut Analytics Server eliminates this burden with a simplified, object-oriented view of data and distributed, memory-based storage. With ScaleOut Analytics Server, developers can easily take advantage of powerful, in-memory analytics to quickly implement their big data applications and near real-time analytics performance.”
In addition to accelerating analytics by integrating map/reduce computation with memory-based data storage, ScaleOut Analytics Server introduces new capabilities that
accelerate the development and deployment of analysis code. One new feature called invocation grids, lets developers automatically pre-stage the software execution environment across grid servers. Combined with automatic code shipping, invocation grids dramatically simplify code deployment, especially in multiple fast-turn scenarios that examine the impact of variable business conditions.
Highlights and features of ScaleOut Analytics Server include:
- Near real-time big data analytics – Parallel data analysis delivers in-memory map/reduce functionality, enabling simple and powerful big data analytics by removing performance bottlenecks. Minimal data motion improves performance, enabling data to be analyzed “in-place,” and results to be returned more quickly.
- Simplified programming model – Anobject-oriented approach with methods and queries written in familiar languages, such as Java and C#, reduces development time and eliminates the need to learn parallel programming techniques or complex APIs. The execution engine automatically optimizes performance and eliminates the need for tuning by the developer.
- Automated code shipping – Invocation grids boost development productivity and fast-turn analytics by automatically pre-staging the development environment, and automatic code shipping delivers analytics code to grid servers for execution.
- Cloud ready – All of ScaleOut Analytics Server’s features are available for use in public and private clouds to leverage the elasticity and massive scalability enabled by cloud computing.
ScaleOut Analytics Server™ is available as a perpetual license, an annual subscription, or usage-based fee in selected public clouds.
For more information, please visit www.scaleout.com
About ScaleOut Software, Inc.
ScaleOut Software develops software products that provide scalable, highly available memory-based storage and analysis for workload data in server farms and compute grids. It has offices in Bellevue, Washington and Beaverton, Oregon. The company was founded by Dr. William L. Bain, whose previous company, Valence Research, developed and distributed Web load-balancing software that was acquired by Microsoft Corporation and is now called Network Load Balancing within the Windows Server operating system.
Please visit www.scaleoutsoftware.com or follow us on Twitter @scaleout_inc for more information.