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Data Warehouse Dimensional Modeling-The data warehouse dimensional modelling are divided

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Data Warehouse Dimensional Modeling-

We know that the large information is stored in data warehouse with different formations schema. It is nothing but the logical description of entire database system. The all database structure are represented with dimensional modelling in data warehousing, All data warehouse tables are related with each other for extracting information with user requirement. The all tables are sharing data from one another with use of primary key and foreign key. The all tables is indicated with Fact and

dimension tables. That fact and dimension tables are represented the whole structure of data warehouse. In dimensional modelling the one fact table is join to another dimensional table for indicating sharing data. Dimensional modelling are designed for reading summarizing and analyzing information from large database.

Fact Table-

Fact table represent business main components or entities like sales,customer,product.

Dimension Table-

Dimension table contains the attribute of fact table that describe the extra information about fact table.

The data warehouse dimensional modelling are divided into-

1.Star Schema

2.Snowflake Schema

3.Galaxy Schema

1.Star Schema-

Star schema is the model architecture of simplest data warehouse structure. In Star schema the fact table is located into center and all dimension table attached to fact table. The fact table represent the entities and dimension table represents extra information about that entities. It is easy to understand simple structure small number of table to join.

2.Snowflake Schema-

Snowflake is a very complex architecture of data warehouse. It is the extraction of star Schema in snowflake schema. The one fact table is attached to multiple dimension tables but each dimension table is split again and join to another dimension table for representing extra information about that dimension table.

3.Galaxy Schema-

In Galaxy schema multiple fact tables are present and that all fact tables are connected with each other for sharing information and all dimension tables are attached to fact table in Galaxy schema. Multiple fact table can access the information from 1 single dimension table.



Explanation :

Dimensional modeling is a key technique used in data warehouse design to optimize data storage and retrieval for analytical processing. It focuses on structuring data in a way that supports fast query performance and intuitive data analysis. Unlike transactional databases that prioritize data consistency and normalization, dimensional modeling emphasizes simplicity and accessibility for decision-making processes.

The core concept of dimensional modeling revolves around two main components: fact tables and dimension tables. Fact tables store quantitative data or measurable metrics such as sales amount, profit, or quantity sold. These facts are typically numeric and represent business events or transactions. On the other hand, dimension tables provide descriptive context for these facts. Dimensions include attributes such as time, customer, location, or product details, which help users analyze data from different perspectives.

A common structure used in dimensional modeling is the star schema, where a central fact table is directly connected to multiple dimension tables. This design simplifies queries and enhances performance. Another variation is the snowflake schema, where dimension tables are further normalized into sub-dimensions to reduce redundancy. However, while snowflake schemas save storage space, they can be slightly more complex and slower for querying compared to star schemas.

Dimensional modeling supports various types of facts such as additive, semi-additive, and non-additive facts, which determine how data can be aggregated. It also introduces the concept of slowly changing dimensions (SCDs) to handle changes in dimension attributes over time—for example, tracking a customer’s address history or changes in product prices.

The main advantage of dimensional modeling is its ability to make complex data easily understandable to business users. It enables efficient Online Analytical Processing (OLAP) operations like roll-up, drill-down, and slice-and-dice, which are essential for business intelligence and reporting.

In summary, dimensional modeling is a vital methodology in data warehouse design that bridges the gap between technical data storage and business analysis. By organizing data into meaningful dimensions and measurable facts, it provides a clear, efficient, and user-friendly framework for decision support and performance analysis in organizations.

Read More-

  1. What Is Data Warehouse
  2. Applications of Data Warehouse, Types Of Data Warehouse
  3. Architecture of Data Warehousing
  4. Difference Between OLTP And OLAP
  5. Python Notes

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