Mastering Data Engineering: An Introduction to Star and Snowflake Schemas
Rakesh Pati
Looking for Remote Data Engineering work | Eager to Collaborate with a Team of Data Engineers
As a data engineer, one of the foundational concepts we need to understand is the structure of databases. Today, let’s dive into two important data modeling techniques: the star schema and the snowflake schema, with a simple example from a grocery store transaction database.
Understanding the Basics
Imagine you own a grocery store. Your store's daily sales transactions are stored in a database, either in an Excel file or a relational database such as MySQL or Oracle. This database contains information about items purchased, customers, payment details, and more. Initially, all this information might be stored in a single table, a highly denormalized database, where everything is crammed into a single row. However, this can lead to data duplication and inconsistency issues.
The Concept of Normalization
To address these issues, we use normalization, where redundant information is stored in separate tables. For example, instead of repeating customer details like John Smith's name and address across multiple rows, we create a separate 'Customer' table with unique customer IDs. Similarly, we have an 'Item' table for products like potatoes and broccoli, each with its own item ID.
Star Schema: A Simple Approach
In a star schema, the main table, called the fact table, stores transactional data, while the dimension tables store descriptive information. Here’s how it looks:
Fact Table: Contains sales data with IDs for customers and items.
Dimension Tables: Separate tables for customers, items, and dates.
The fact table is at the center, and the dimension tables radiate outwards like points on a star. This structure optimizes data storage and simplifies query performance.
领英推荐
Snowflake Schema: Extending the Concept
The snowflake schema takes the star schema a step further. In this model, one or more dimension tables are further normalized into additional tables (sub-tables) to reduce redundancy and ensure data integrity. The Fact Table is at the center, containing transactional data. The resulting structure resembles a snowflake, , providing a more normalized and efficient data storage solution.
Conclusion
Understanding star and snowflake schemas is essential for effective data modeling and analytics. The star schema offers simplicity and performance, while the snowflake schema provides a normalized structure to reduce data redundancy.
Thank you for taking the time to read the full article. I hope you found it insightful and enjoyable. If you liked it, please leave a like and share your thoughts in the comments. Your feedback means a lot!
Feel free to reach out!
Get in Touch
- ?? Email: [email protected]
- ?? LinkedIn: https://www.dhirubhai.net/in/rakesh-pati-050492167/
#DataModeling #DatabaseManagement #DataEngineering #StarSchema #SnowflakeSchema #KnowledgeSharing