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.
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
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.