The computing scenario of analyzing large-scale data applications has become a challenge for managerial decision-making. The state-of-the art business processes require the enterprise executives to take to Data Warehouse (DWH) systems, lest their decisions are unstructured and losing. This paper gives an overview of data warehousing and pattern discovery techniques for business intelligence in taking selective decisions based on the business data. It also explains three solutions to handling ELDB systems for enterprise applications, and gives the related product details.

Various kinds of Information Systems, like Management Information Systems (MIS), Enterprise Information Systems (EIS), Executive Information Systems (EIS), Geographical Information Systems (GIS, like Spatial Information Systems), Decision Support Systems (DSS), Data Warehouse (DWH) Systems, etc., have been in the making since the last few decades. However, data usage has kept rising by leaps and bounds and led to the emergence of Extremely Large Databases (ELDBs) in lieu of Very Large Databases (VLDBs). The data volume in a VLDB system is in the range of 100 GB-100 TB (1024 GB). According to the GTE Research Center Report (GTE, 2000), scientific and academic organizations store approximately, 1 TB of new data each day, even though the academic community is not the leading supplier of new data worldwide. The ever-increasing volumes of data in businesses and organizations that accumulate and require processing, led to the emergence of ELDB systems which have data volumes even larger than 100 TB. The executives who are at the helm of affairs in decision-making are now at a mandate to use specialized decision support systems. Consequently, the arena of Information Systems has seen the emergence of EISs since the late 1970s. DSSs are a means for business intelligence and competitive advantage. DWH systems are the modern kind of DSSs.

The rest of the paper is structured as follows: The next section gives a taxonomy of knowledge and intelligence. Section 3 explains the significance of competitive advantage peculiar to ELDB systems. Section 4 details a survey of three DWH and Data Mining (DM) solutions for business intelligence through ELDB systems in terms of Cognos Impromptu, Informatica and Clementine tools.

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