Data warehouse is an established concept and discipline that is discussed in books, conferences and seminars. Indeed data warehouses are a standard feature of modern corporations. Corporations use data warehouses to make business decisions every day. In a word, the data warehouse represents “conventional wisdom” and is a standard part of the corporate infrastructure.
Into this world comes a new technology – Big Data. In some ways Big Data competes (or thinks that it competes) with data warehousing. Indeed there are some similarities between Big Data and a data warehouse. Big Data and data warehouses both hold data electronically. Big Data and data warehouses both hold lots of data. Big Data and data warehouses both hold data that can be used for decision making. So it is natural for vendors of Big Data to proclaim that with Big Data you don’t need a data warehouse. At least that is the impression that many Big Data vendors seem to give.
This whitepaper describes data warehouses, big data, an architecture, a technology, harmonious coexistence, repetitive and non-repetitive data, the great divide, data modeling, textual disambiguation, context enriched big data, two of data in data warehouses, a new type of analytical processing, archival data to big data, doing analytics, data marts and the dimensional model, what about modeling, and the system of record.
Author: William Inmon
William Inmon of Castle Rock, Colorado, is the father of data warehouse and the developer of textual disambiguation at Forest Rim Technology. Bill has written 56 books translated into 9 languages. Bill was named as one of the ten most influential people in the history of computing by ComputerWorld in 2007.