The big story in analytics and information management in 2011 was Big Data. In 2012, the trend is accelerating. At the center of discussions about managing huge amounts of novel sources of information is Hadoop. One can perceive this effect as ousting data warehouses as the premier location for the gathering and analyzing of data, but it is only partially true. While the capabilities and applications of Hadoop have been clearly demonstrated for organizations that deal with massive amounts of raw data as their primary business, especially web-oriented ones, where it fits in other kinds of organizations is not quite as clear. The core of Hadoop is a programming framework, MapReduce, which crunches data from other sources and can deliver analysis and reporting (or sometimes just aggregated data to be used in other analytical systems). Because Hadoop consumes "used" data, its application may tend to overlap with the way analytical processing has been done for decades -- with data integration tools, data warehouses and a variety of reporting, analytical and decision support tools. It is for that reason that some Hadoop proponents see it as a replacement for the current analytic architecture such as data warehousing.
To put this in perspective, it is important to understand what analytics means and how it is used. In this presentation we present a formal definition of analytical "types" in order to exemplify the kinds of analytics and analytical processes that organizations employ. This helps to pinpoint where Hadoop is appropriate, where existing data warehousing and business intelligence environments are appropriate. There is a third option as well, with the emergence of new, hybrid systems that are relational databases with MapReduce capabilities such as the Teradata Aster, Greenplum, Hadapt and many others. Others are also emerging and competitors to Hadoop itself are already in business.
