Working with surrogate keys in Snowflake schema

05 Oct 2023

In data warehousing, a Snowflake schema is a widely used dimensional model for organizing data. It consists of a central fact table surrounded by dimension tables, which are further interconnected. One key feature of Snowflake schemas is the use of surrogate keys.

Surrogate keys are system-generated unique identifiers used to uniquely identify each record in a dimension table. Unlike natural keys, which are based on the attributes of the data itself, surrogate keys have no inherent meaning. They simplify data management by providing a consistent value that can be used across tables and schemas.

In this blog post, we will explore how to work with surrogate keys in a Snowflake schema and understand their importance in data warehousing.

Why Use Surrogate Keys

There are several reasons why surrogate keys are favored over natural keys in Snowflake schemas:

Data Integrity: Surrogate keys keep the primary key values separate from the actual data attributes. This eliminates the risk of data duplication or inconsistencies caused by changes in the natural key values.
Performance: Surrogate keys are typically small, numeric, and sequential, making them ideal for indexing and efficient querying. Compared to larger natural keys, they optimize storage and improve query performance.
Flexibility: Since surrogate keys have no inherent meaning, they can be easily changed or replaced without affecting the relationships between tables. This flexibility is especially useful when handling dimension table updates or data migrations.

Creating Surrogate Keys in Snowflake Schema

To create surrogate keys in Snowflake schema, you can follow these steps:

Add a Surrogate Key Column: In each dimension table, add a surrogate key column. This column should be ordered, autogenerated, and unique for each record.
```
CREATE TABLE customer_dim (
    customer_id INT IDENTITY(1,1),
    customer_name VARCHAR,
    ...
);
```
Populate the Surrogate Key: When inserting data into the dimension table, omit the surrogate key column from the insert statement. The Snowflake database will automatically generate and populate the unique surrogate key values.
```
INSERT INTO customer_dim (customer_name, ...)
VALUES ('John Doe', ...);
```

Reference the Surrogate Key: In the fact table, refer to the surrogate keys from the corresponding dimension table to establish relationships.

CREATE TABLE sales_fact (
    ...
    customer_id INT,
    ...
    FOREIGN KEY (customer_id) REFERENCES customer_dim(customer_id)
);

Querying Data with Surrogate Keys

When querying data in a Snowflake schema, you can use surrogate keys to join the fact table with dimension tables. This allows you to easily retrieve information from various dimensions without relying on natural keys.

SELECT *
FROM sales_fact
JOIN customer_dim ON sales_fact.customer_id = customer_dim.customer_id
WHERE ...

By leveraging surrogate keys, you can efficiently retrieve and analyze data from the Snowflake schema, ensuring accurate and consistent results across various reports and queries.

Conclusion

Surrogate keys play a crucial role in maintaining data integrity and optimizing performance in Snowflake schema. By using system-generated unique identifiers, you can simplify data management, enhance query performance, and facilitate updates or migrations. Incorporating surrogate keys in your Snowflake schema design will ensure a robust and efficient data warehousing solution.

#datawarehousing #surrogatekeys