data mining

This is the 3rd part of a series of posts on Big Data. Read Part-1 (What is Big Data) and Part-2 (Hadoop). Traditionally data warehouses have been built with relational databases as backbone. With the new challenges (3Vs) of Big Data, relational databases have been falling short of the requirements of handling New data types (unstructured data) Extended analytic processing Throughput (TB/hour loading) with immediate query access The industry has turned to Hadoop as a disruptive solution for these very challenges.

By definition Business Intelligence (BI) has been about making decisions based on information obtained from meaningful data. At one point of time we had terms like decision support systems (DSS) to define technology which has finally evolved into BI as we know it now. Or has it? Somewhere along the line, we became entangled in the technology aspect of it and with all the buzzwords of data warehousing, data mining, dimensional modeling, data marts, **CRM **and **SCM **it is not difficult to see why.