Data Warehousing Basic Questions

What is Fact Table?

Fact Table is

a) Completely Normalize

b) Partially Normalized

c) Completely De-normalized

d) Partially De-normalized

What is a Data Warehousing?

Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources.

Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases.

What are Data Marts?

Data Mart is a segment of a data warehouse that can provide data for reporting and analysis on a section, unit, department or operation in the company, e.g. sales, payroll, production. Data marts are sometimes complete individual data warehouses which are usually smaller than the corporate data warehouse.

What is a Star Schema?

A relational database schema organized around a central table (fact table) joined to a few smaller tables (dimension tables) using foreign key references. The fact table contains raw numeric items that represent relevant business facts (price, discount values, numberof units sold, dollar value, etc.)

What is Dimensional Modelling?

Dimensional Modelling is a design concept used by many data warehouse desginers to build thier datawarehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of measuremnets ie, the dimensions on which the facts are calculated.

What is Snow Flake Schema?

Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance