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Data warehousing Architecture

Monday, April 28, 2008

Galaxy Schema

Galaxy Schema is combination of more than one star schema.

Snow flake Schema


The Snow Flake is also the simplest data warehouse schema. Because the diagram resembles a same as star schema .But only difference is dimension table is surrounded by sub-dimension table in snow flake schema

Star Schemas


The Star Shema is the one of the simplest data warehouse schema. Because the diagram resembles a star, with points radiating from a center. The center of the star consists of one or more fact tables and the points of the star are the dimension tables.

Data Warehousing Schemas

A schema is a collection of database objects, including tables, views, indexes, and synonyms. You can arrange schema objects in the schema models designed for data warehousing in a variety of ways. The model of your source data and the requirements of your users help you design the schema. We can sometimes get the source model from your company's enterprise data model and reverse-engineer the logical data model for the data warehouse from that. The physical implementation of the logical data warehouse model may require some changes to adapt it to your system parameters—size of machine, number of users, storage capacity, type of network, and software.There are three types of schema
1) Star Shema
2) Snow flake Shema
3) Galaxy Shema

Data Warehouse Architecture (with a Staging Area and Data Marts)


We can do this by adding data marts, which are systems designed for a particular line of business. For example where purchasing, sales, and inventories are separated. In this example, a financial analyst might want to analyze historical data for purchases and sales

Data Warehouse Architecture (with a Staging Area)


We need to clean and process our operational data before putting it into the warehouse. So we can do this programmatically, although most data warehouses use a staging area instead. A staging area simplifies building summaries and normal data warehouse management
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