In data warehousing, the snowflake schema is a popular multidimensional data model that organizes data into a central fact table surrounded by multiple dimension tables. This schema provides a more normalized structure compared to the star schema, allowing for improved data integrity and flexibility in analysis. While the snowflake schema is effective in many cases, there are scenarios where variants of the schema can be used to enhance performance or accommodate specific data requirements.

In this blog post, we will explore three common variants of the snowflake schema and illustrate how they can be implemented in SQL.

1. Star Schema

The star schema is the simplest variant of the snowflake schema. It consists of a single fact table connected directly to several dimension tables. Each dimension table is connected to the fact table through a foreign key relationship, forming a star-like shape. This variant is widely used because of its simplicity and ease of querying.

CREATE TABLE fact_table (
    fact_id INT,
    dimension_id INT,
    measure_1 FLOAT,
    measure_2 FLOAT,
    ...
);

CREATE TABLE dimension_table_1 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

CREATE TABLE dimension_table_2 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

2. Extended Star Schema

The extended star schema expands upon the star schema by introducing additional levels of dimension tables. These intermediate dimension tables help break down the hierarchy of attributes within a dimension, leading to more granular analysis. By doing so, it can improve query performance and provide more detailed insights into the data.

CREATE TABLE fact_table (
    fact_id INT,
    dimension_id INT,
    measure_1 FLOAT,
    measure_2 FLOAT,
    ...
);

CREATE TABLE dimension_table_1 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

CREATE TABLE dimension_table_2 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

CREATE TABLE dimension_table_3 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

3. Galaxy Schema

The galaxy schema is an extension of the snowflake schema that combines multiple star schemas. It is suitable for complex data models with multiple fact tables. Each star schema within the galaxy schema can have its own set of dimension tables, allowing for independent analysis across different subject areas.

CREATE TABLE fact_table_1 (
    fact_id INT,
    dimension_id INT,
    measure_1 FLOAT,
    measure_2 FLOAT,
    ...
);

CREATE TABLE fact_table_2 (
    fact_id INT,
    dimension_id INT,
    measure_1 FLOAT,
    measure_2 FLOAT,
    ...
);

CREATE TABLE dimension_table_1 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

CREATE TABLE dimension_table_2 (
    dimension_id INT,
    dimension_attribute_1 VARCHAR,
    dimension_attribute_2 VARCHAR,
    ...
);

Conclusion

Understanding different variants of the snowflake schema can help architects and developers design more efficient and flexible data warehouses. By leveraging star schema, extended star schema, or even galaxy schema, organizations can optimize performance, accommodate diverse data requirements, and gain deeper insights from their data.

By utilizing these SQL examples, you can easily implement these schema variants in your data warehouse solutions.

#snowflakeschema #datamodelling