Google published a paper on MapReduce in 2004, which talked about simplified data processing on large clusters. In this paper, Google SHARED their PROCESS for transforming large volumes of web data content into search indexes using low-cost commodity CPUs. It was Google’s use of MapReduce that encouraged the use of low-cost commodity hardware for such huge applications. Google extended the map-reduce concept to reliably execute on billions of web pages on hundreds or THOUSANDS of low-cost commodity CPUs.

This RESULTED into building a system that would EASILY scale as their data increased without forcing them to purchase expensive hardware. That’s where Google invented BigTable to boost their search capabilities. That was first real use of NoSQL columnar data store running on commodity hardware which made a big impact in NoSQL drive.

Google published a paper on MapReduce in 2004, which talked about simplified data processing on large clusters. In this paper, Google shared their process for transforming large volumes of web data content into search indexes using low-cost commodity CPUs. It was Google’s use of MapReduce that encouraged the use of low-cost commodity hardware for such huge applications. Google extended the map-reduce concept to reliably execute on billions of web pages on hundreds or thousands of low-cost commodity CPUs.

This resulted into building a system that would easily scale as their data increased without forcing them to purchase expensive hardware. That’s where Google invented BigTable to boost their search capabilities. That was first real use of NoSQL columnar data store running on commodity hardware which made a big impact in NoSQL drive.