The Internet lacks an ‘easy to search’ definition of grid-based data.
This technology is starting to feature more prevalently than ever before, especially in the realm of in-memory data grids (IMDGs) which are used in big data analysis environments such as Hadoop.
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So then, by way of a definition:
Data grids combine distributed caching with in-memory analysis and management tools to provide a solution for managing fast-changing data in a server farm, compute grid, or in the cloud. This technology typically features powerful APIs for data access, query and analysis along with supporting management tools.
In-memory data grid (IMDG) solutions are typically found deployed in financial services, e-commerce and other mission-critical applications.
According to ScaleOut Software, “[IMDG technology] opens the door to the next generation of scalable application performance and parallel data analysis – and take full advantage of the cloud-computing revolution.”
Image credit: GridGain
According to GridGain, “In-Memory Data Grid is the core technology behind GridGain’s capability to process large data sets with low latency in Real Time context. Easily scaling from a single computer to terabytes of data and thousands of nodes GridGain In-Memory Data Grid technology provides capability to parallelize the data storage by storing partitioned data in in-process memory – the closest location the data can theoretically reside in relation to the application using it.