How to Choose Between Star Schema and Snowflake Schema in Oracle

What is the difference between star and snowflake schema in Oracle?

Star schema keeps dimensions denormalized for speed, while snowflake normalizes them for space and consistency.

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Description

What is a star schema?

A star schema stores a central fact table surrounded by denormalized dimension tables. Each dimension connects to the fact table with a single primary-to-foreign key, resulting in fewer joins and faster scans.

What is a snowflake schema?

A snowflake schema starts with the same fact table but normalizes each dimension into multiple related tables. This saves storage and enforces data integrity but adds joins.

When should I use a star schema in Oracle?

Choose a star schema when query speed is critical, the dataset fits in memory, and dimensions rarely change. Oracle’s bitmap indexes, materialized views, and partition pruning thrive on the flatter layout.

When is a snowflake schema better?

Pick a snowflake schema when many dimensions share hierarchies (e.g., Country → State → City) or when dimension data changes frequently. Normalization prevents update anomalies and reduces redundant storage.

How does query performance differ?

Star schemas require fewer joins, so OLAP queries like SUM(total_amount) by product and month run faster. Snowflake schemas may need 3–4 joins per dimension and depend on optimizer statistics, indexes, and join elimination.

How do I model an Order fact table?

Star: Orders_Fact joins directly to Customers_Dim, Products_Dim, and Date_Dim.
Snowflake: Orders_Fact joins to Customers_Dim → Geography_Dim and Products_Dim → Category_Dim.

Can I mix both designs?

Yes—hybrid schemas keep high-cardinality or volatile dimensions snowflaked while leaving stable dimensions denormalized.

Best practices

Use surrogate keys instead of business keys. Partition large fact tables. Add bitmap indexes on low-cardinality columns. Refresh optimizer statistics regularly.

Code examples

-- Star-style dimension
CREATE TABLE Products_Dim (
product_key NUMBER PRIMARY KEY,
name VARCHAR2(100),
category VARCHAR2(50),
price NUMBER(10,2),
stock NUMBER
);

-- Snowflake extension
CREATE TABLE Categories_Dim (
category_key NUMBER PRIMARY KEY,
category VARCHAR2(50)
);
ALTER TABLE Products_Dim ADD CONSTRAINT fk_cat
FOREIGN KEY (category) REFERENCES Categories_Dim(category);

Why How to Choose Between Star Schema and Snowflake Schema in Oracle is important

How to Choose Between Star Schema and Snowflake Schema in Oracle Example Usage


-- Compare revenue by country in both schemas
SELECT g.country, SUM(o.total_amount) AS revenue
FROM   Orders_Fact o
JOIN   Customers_Dim c  ON c.customer_key = o.customer_key
JOIN   Geography_Dim g  ON g.geo_key     = c.geo_key  -- snowflake join
GROUP  BY g.country
ORDER  BY revenue DESC;

How to Choose Between Star Schema and Snowflake Schema in Oracle Syntax


-- Star schema fact table
CREATE TABLE Orders_Fact (
    order_key     NUMBER PRIMARY KEY,
    customer_key  NUMBER REFERENCES Customers_Dim(customer_key),
    product_key   NUMBER REFERENCES Products_Dim(product_key),
    date_key      DATE,
    total_amount  NUMBER(12,2)
);

-- Snowflake variant of Customer dimension
CREATE TABLE Customers_Dim (
    customer_key NUMBER PRIMARY KEY,
    name         VARCHAR2(100),
    email        VARCHAR2(255),
    geo_key      NUMBER
);
CREATE TABLE Geography_Dim (
    geo_key   NUMBER PRIMARY KEY,
    country   VARCHAR2(60),
    state     VARCHAR2(60),
    city      VARCHAR2(60)
);
ALTER TABLE Customers_Dim ADD CONSTRAINT fk_geo
  FOREIGN KEY (geo_key) REFERENCES Geography_Dim(geo_key);

Common Mistakes

Over-normalizing small dimensions into snowflake form slows queries without real space savings. Fix: keep low-cardinality, stable dimensions denormalized.
Ignoring query patterns; choosing star or snowflake solely on theory. Fix: prototype both, gather execution plans, and pick the design that meets SLA.