How to Choose Between Star and Snowflake Schema in PostgreSQL

Why pick a star or snowflake schema?

Star schemas denormalize dimension tables for faster joins and simpler SQL. Snowflake schemas normalize dimensions into sub-tables, cutting storage and update anomalies. Pick the model that best balances query speed, maintenance effort, and disk cost.

When does a star schema outperform a snowflake?

Star schemas excel in read-heavy analytics where joins dominate runtime.Fewer joins let the planner push down filters and exploit indexes, boosting dashboard speed.

When is a snowflake schema better?

Choose snowflake when dimension data changes often, must avoid duplication, or when storage costs matter. Normalized tables let updates cascade cleanly and reduce disk use.

How to create a star schema for ecommerce data?

Create one fact table (Orders) and wide dimension tables (Customers, Products, Calendar).Fact rows hold foreign keys to each dimension.

Star example

-- fact
CREATE TABLE Orders (
id SERIAL PRIMARY KEY,
customer_id INT,
product_id INT,
order_date DATE,
total_amount NUMERIC(10,2)
);
-- dimensions
CREATE TABLE Customers (
id SERIAL PRIMARY KEY,
name TEXT,
email TEXT,
created_at TIMESTAMP
);
CREATE TABLE Products (
id SERIAL PRIMARY KEY,
name TEXT,
price NUMERIC(8,2),
stock INT
);


How to snowflake the same model?

Split low-cardinality attributes into sub-tables.Customers gets a Geography table; Products gets a Category table. Facts still reference leaf dimension tables.

Snowflake example

CREATE TABLE Geography (
id SERIAL PRIMARY KEY,
country TEXT,
region TEXT
);
ALTER TABLE Customers ADD COLUMN geography_id INT;


Which SQL changes after modeling?

Star queries join fewer tables; snowflake queries add joins for each normalized level. Index foreign keys to keep performance acceptable.

Best practices for hybrid models?

Start star, then snowflake only the high-cardinality or frequently changing dimensions.Measure query plans before and after refactors.

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