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Using In-Memory Computing to Simplify Big Data Analytics
Today’s big data revolution has brought about the need to quickly analyze large datasets for important patterns and trends. Parallel computing techniques, such as ‘map/reduce,’ have opened the door to dramatically reducing analysis times and are now proliferating in platforms such as open source Hadoop.
But the complexity of these techniques can be daunting, impeding adoption and inhibiting widespread use.
The technology of in-memory data grids (IMDGs) offers important breakthroughs in addressing the challenge. This resource explains how IMDGs reduce complexity and lower the barrier to big data analysis, and are able to:
- Lower the learning curve
- Shorten the development cycle
- Reduce analysis time
- And more.