Data Warehouse

Various Data Warehouse Design Approaches:Top-Down and Bottom-Up

Data Warehouse design approaches are very important aspect of building data warehouse. Selection of right data warehouse design could save lot of time and project cost. There are two different Data Warehouse Design Approaches normally followed when designing a Data Warehouse solution and based on the requirements of your project you can choose which one suits your particular scenario. These methodologies are a result of research from Bill Inmon and Ralph Kimball. Bill Inmon - Top-down Data Warehouse Design Approach “Bill Inmon” is sometimes also referred to as the "father…

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September 17, 2016
Data Warehouse

Types of Dimension Tables in a Data Warehouse

A dimension is something that qualifies the quantity or measures. Dimensions store the textual descriptions of the business. With help of dimension you can easily identify the measures.The different types of dimension tables are available as below: Read: Types of Fact Tables in a Data Warehouse Data Warehouse Fact Constellation Schema and Design Data Warehouse Three-tier Architecture in Details Types of Dimension Tables in a Data Warehouse Below are the commonly used dimension tables in data warehouse: Conformed Dimension A conformed dimension is the dimension that is shared across multiple data…

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September 16, 2016
Data Warehouse

Types of Fact Tables in a Data Warehouse

A fact table stores quantitative information for analysis and is often denormalized. A fact table holds the measures, metrics and other quantifiable information. The different types of fact tables are as explained below: Read: Data Warehouse fact-less fact and Examples Slowly changing dimension Types of Dimension Tables in a Data Warehouse Types of Facts There are three types of facts: Additive Facts Additive facts can be used with any aggregation function like Sum(), Avg() etc. Example is Quantity, sales amount etc. Semi Additive Facts Semi-additive facts are those where only…

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September 16, 2016
Data Warehouse

Data Warehouse Snowflake Schema Model and Design

Data warehouse Snowflake schema is extension of star schema data warehouse design methodology, a centralized fact table references to number of dimension tables, however, one or more dimension tables are normalized i.e. dimension tables are connected with other dimension tables. Primary Keys from the dimensions flows into fact table as foreign key. Star Schema model in Data Warehouse Data Warehouse Fact Constellation Schema and Design Snowflake schema increases the level of normalization in data, the dimension table is normalized into multiple tables. This schema has a disadvantage in terms of data retrieval, we…

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August 3, 2016
Data Warehouse

Step by Step Guide to Dimensional Data Modeling

In this post, you will learn about the step by step guide to dimensional data modeling. You will see how to use dimensional modeling technique in real life scenarios. What is Dimensional data Modeling? Dimensional data modeling is one of the data modeling techniques used in data warehouse design. The main goal of this modeling is to improve the data retrieval, it is optimized for the SELECT operation. Dimensional data modelling is best suited for the data warehouse star and snow flake schema. Dimensional data modeling in data warehouse is different than the…

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August 1, 2016
Data Warehouse

Data Warehouse Star Schema Model and Design

Data warehouse Star schema is a popular data warehouse design and dimensional model, which divides business data into fact and dimensions. In this model, centralized fact table references many dimension tables and primary keys from dimension table flows into fact table as a foreign key. This entity-relationship diagram looks star, hence the name star schema. This model divides the business data into fact which holds the measurable data, and dimension that holds descriptive attributes related to the fact data. For examples, fact data includes price, quantity, weight measurements and related dimension attributes example includes product color, sales…

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July 30, 2016