Concepts

What is the main difference between Inmon and Kimball philosophies of data warehousing?

Concepts

Written by DataGuru Wednesday, 19 May 2010 03:10

Both differed in the concept of building the datawarehosue. Kimball views data warehousing as a constituency of Data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtain from the dimension modeling on a local departmental level. Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise.
 

What is Hierarchy in data warehouse terms?

Concepts

Written by DataGuru Wednesday, 19 May 2010 03:10

Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure. Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies–one for product categories and one for product suppliers. Dimension hierarchies also group levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse. When designing hierarchies, you must consider the relationships in business structures. For example, a divisional multilevel sales organization. Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

What are the differnces between a RDBMS schema and a data warehouse schema?

Concepts

Written by DataGuru Wednesday, 19 May 2010 03:10

RDBMS Schema * Used for OLTP systems * Highly Normalized * Difficult to understand and navigate * Difficult to extract and solve complex problems DWH Schema * Used for OLAP systems * De-normalized * Easy to understand and navigate * Relatively easier in extracting the data and solving complex problems
 

What is meant by metadata in the context of a Data warehouse?

Concepts

Written by DataGuru Wednesday, 19 May 2010 03:10

Meta data is the data about data; Business Analyst or data modeler usually capture information about data – the source (where and how the data is originated), nature of data (char, varchar, nullable, existance, valid values etc) and behavior of data (how it is modified / derived and the life cycle ) in data dictionary a.k.a metadata. Metadata is also presented at the Datamart level, subsets, fact and dimensions, ODS etc. For a DW user, metadata provides vital information for analysis / DSS.

What are the commonly used indexes in Data warehouse systems?

Concepts

Written by DataGuru Wednesday, 19 May 2010 03:10

B-Tree and Bit Map indexes.
 

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