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This analysis results in data generalization and data mining.

Data mining functions such as association, clustering, classification, prediction can be integrated with OLAP operations to enhance the interactive mining of knowledge at multiple level of abstraction.

In a table, a row corresponds to a record with a set sequence of data fields, while a column lists one given data field for all the records.

The data is structured in that only the “right” kind of data can be used in a given field: for example, in a customer relational database, a shipping date cannot be used in a field for a delivery address, and so on.

The term "Data Warehouse" was first coined by Bill Inmon in 1990.

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The relational database used with many applications and systems holds data in tables of rows and columns.

Along with generalized and consolidated view of data, a data warehouses also provides us Online Analytical Processing (OLAP) tools.

These tools help us in interactive and effective analysis of data in a multidimensional space.

The structure or “schema” of a relational database is defined before starting to record data. However, by organizing the database as separate tables with defined relationships between them, structured data can be accessed or reassembled in many different ways.

By comparison, the need to handle unstructured data has led to the creation of other types of databases.

I have spent intimate time with both camps and can help illuminate the position for each viewpoint. Perhaps some background on each side is in order, since history often lends perspective.

The concept of data warehousing actually dates back more than 25 years to Massachusetts Institute of Technology research on the requirements of analytic processing versus transaction processing.

However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system.

Many references to data warehousing use this broader context.

As data and analytics become a more integral part of business processes in an organization, so the non-DBAs among us might start to feel lost in a sea of technical terms which are frequently thrown around by technical teams.

The disproportionately loud vendor noise that exists in this space further generates jargon, hype and confusion (just try to get a straight answer to “what is big data”).