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Dimensional Modeling

Dimensional modeling is like organizing data into a set of building blocks (dimension tables) and using those blocks to construct a bigger picture (fact table), making it easier to see patterns and relationships in the data.

In dimensional modeling, you'd have two types of tables: fact tables and dimension tables. The fact table would contain the core data you're analyzing, like sales amounts. The dimension tables would contain additional information about the things you're analyzing, like customers, products, and dates.

In a data warehouse, facts and dimensions are two fundamental components used to organize and structure data for analytical purposes. They form the backbone of a dimensional model, which is a common approach to data modeling in data warehousing. Here's an explanation of facts and dimensions:

Facts

Dimensions



 

Facts and Dimensions:

In a data warehouse, facts and dimensions are two fundamental components used to organize and structure data for analytical purposes.

They form the backbone of a dimensional model, which is a common approach to data modeling in data warehousing.

Facts represent measurable, numeric data that constitute the core metrics of business operations, such as sales revenue or quantities sold. Dimensions, on the other hand, are descriptive attributes that provide context to the facts, often representing the who, what, where, when, and how aspects of the business. Facts are typically stored in fact tables, while dimensions are stored in dimension tables, forming a star schema or snowflake schema. This approach facilitates efficient querying and analysis by organizing data into a structured and intuitive format conducive to business reporting and analytics.

In a dimensional model, facts and dimensions are organized into a star schema or a snowflake schema:

Star Schema

Snowflake Schema




Star Schema

•        In a star schema, the fact table sits at the center, surrounded by dimension tables radiating outwards like the points of a star.

•        Each dimension table is directly linked to the fact table through foreign key relationships.

•        This structure simplifies querying and analysis, as it allows for straightforward joins between the fact table and individual dimension tables.



Snowflake Schema

•        A snowflake schema is similar to a star schema but with normalized dimension tables. This means that dimension tables are further broken down into sub-dimensions, creating a more complex hierarchy.

•        While snowflake schemas may offer better data normalization and storage efficiency, they can introduce additional complexity to queries and may impact performance.




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