Hadoop vs Grid Computing

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Well, Is it really too late to describe the differences between Hadoop and Grid computing?? I think no, that's why I am going to list few key differences between them. So that you can easily figure it out why "Grid Computing getting obsolete"

Yes you are right it is about "Why Grid Computing getting obsolete"
Nowadays, Hadoop is getting on demand and becomes the industry favourite one although same can be done with Grid computing also and even it was, but nowadays it getting obsolete. Let's see below are the key differences between these two power pack technologies.

Hadoop Vs Grid Computing

1. Grid Computing - Works well for predominantly compute intensive jobs, but it becomes a problem when nodes need to access larger data volumes (hundreds of gigabytes), since the network bandwidth is the bottleneck and compute nodes become idle. Whereas, Hadoop - Tries to co-locate the data with the compute nodes, so data access is fast because it is local.This feature, known as data locality(heart of Hadoop).
2. Grid Computing - Data flow is exposed by low level programming. Whereas, In Hadoop all about high level programming.
3. Grid Computing - Explicitly manage their own check pointing and recovery of tasks. Whereas In Hadoop managed by Map Reduce processing engine.
4. Grid Computing resources are highly expensive as compared to Hadoop

Some Grid based products like DataSynapse,Oracle Coherence are popular but very expensive to license.
Oracle Coherence is the in-memory data grid solution that enables organizations to predictably scale mission-critical applications by providing fast access to frequently used data. It's not an open source.

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