Would you run your analytics today off the tape drives? That’s what you do when you use Hadoop MapReduce.
The fundamental flaw in Hadoop MapReduce is an assumption that a) storing data and b) acting upon data should be based off the same underlying storage.
What Hadoop does is offering an approach for problems where having all data in memory is almost impossible and definitely not cost effective. What GridGain data grid does is offering an approach where having data in memory is cost effective. None of these assumptions are fundamental flaws.
The only fundamental flaw is positioning a product by making the wrong assumptions about alternative solutions. Like we’ve seen it before: NoSQL Wants To Be Elastic Caching When It Grows Up… Does It Really? or In-Memory Elastic Databases.
Original title and link: GridGain and Hadoop: About Fundamental Flaws ( ©myNoSQL)