Fast and intuitive in-memory computing platform for live, operational data streams
Beyond just serving as a fast, scalable repository for live data, in-memory data grids (IMDGs) provide the foundation for real-time analytics. By harnessing the scalable computing power of the server clusters on which they run, IMDGs enable large, in-memory data sets to be analyzed in parallel, delivering immediate results and important feedback to live systems. While they can serve distributed queries to select data of interest for client applications, their real power lies in the ability to host data-parallel computations within the grid — moving computing to where the data lives — to deliver blazing performance and eliminate bottlenecks to scalability.
Operational intelligence uses the power of in-memory computing to track fast-changing data within live systems, analyze them in parallel, and then provide immediate insights and actionable feedback — with far-reaching benefits.
Avoid the complexity and performance bottlenecks of conventional stream and event processing systems. With its ability to move computing to where the data lives, in-memory computing introduces the next-generation architecture for streaming analytics.
ScaleOut’s in-memory computing platform brings real-time analytics on live data to the world of big data.
Unlike other stream-processing and CEP architectures, ScaleOut’s in-memory data grid supports the creation of dynamic, object-oriented models for operational systems.
ScaleOut StateServer Pro runs object-oriented, data-parallel computations for both Java/Linux and C#/.NET applications. ScaleOut hServer executes standard Hadoop MapReduce code on live, in-memory data.
ScaleOut’s intuitive in-memory computing platform provides built-in scalability and integrated high availability to enable immediate feedback for live systems and handle fast-growing workloads.
ScaleOut makes in-memory computing easy to use. Experience the low TCO resulting from simplified development, a self-tuning architecture, automatic load-balancing, and transparent high availability.
ScaleOut’s in-memory computing technology scales linearly to handle large workloads fast. Just add servers to the compute cluster, and ScaleOut takes care of the details.
Use in-memory computing to track and analyze live data within a single system. This avoids the performance-killing data motion of conventional stream processing or offline analysis, and it enables immediate insights.
ScaleOut’s object-oriented, data-parallel computing model makes development fast and easy by simplifying application logic and keeping it cleanly separated from the execution platform.
Integrating in-memory data storage and computing moves the focus from pure event processing to modeling live systems, enabling fast, context-aware analysis of incoming data streams.
Run MapReduce applications on in-memory data with breakthrough performance. See how ScaleOut hServer can be used to extract, transform, and load live data into HDFS.
Use the power of in-memory computing in minutes on Windows or Linux.
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