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It's very easy to explain NoSQL from high level view - it is basically "key-value" storage. Of course with thousand minor and important things, but in general it's just key value storage.
What's the best way to explain Hadoop and Map/Reduce? May be some "real world" example which can be easy to give an compare for even newbies? Thanks!
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The Apache Hadoop is a software that allows all the distributed processing of large data sets across clusters of computers using simple programming. MapReduce is a programming model which is an implementation for processing and generating big data sets with distributed algorithm on a cluster.
MapReduce is a programming model or pattern within the Hadoop framework that is used to access big data stored in the Hadoop File System (HDFS). It is a core component, integral to the functioning of the Hadoop framework.
MapReduce is a programming paradigm that enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. As the processing component, MapReduce is the heart of Apache Hadoop. The term "MapReduce" refers to two separate and distinct tasks that Hadoop programs perform.
The main difference between HDFS and MapReduce is that HDFS is a distributed file system that provides high throughput access to application data while MapReduce is a software framework that processes big data on large clusters reliably.
I recently found this great article describing Map Reduce :
I’ve been planning on writing about the Google’s MapReduce algorithm for some time but I couldn’t find a good practical example. Then we had a Northwest C++ Users Group presentation by Steve Yegge and a followup discussion and beers, and I had a little epiphany. Steve was talking about, among other things, the build process. And that’s just a bunch of algorithms that are perfect for explaining MapReduce.
The code examples are in C++, but the content is really language agnostic.
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