3NF vs Star Schema

The best overall data architecture for a data warehouse is multi-layered. Having multiple layers makes it possible to solve different problems independently and leads to the simplest and cheapest maintenance in the long term. Multi-layered data warehouse architectures typically have at least three layers:

• Data Acquisition Layer: This initial layer is responsible for extracting raw data from various sources. It ensures that the data is captured efficiently and accurately for further processing.
• Data Integration Layer: The middle layer focuses on transforming, cleaning, and integrating data from multiple sources. This layer commonly implements a Third Normal Form (3NF) data model, which organizes data to reduce redundancy and improve data integrity.
• Presentation Layer: This layer transforms the integrated data into a format most suitable for simple end-user queries. The Star Schema, with its central fact tables and associated dimension tables, is typically utilized here to ensure data is easy to query.

Since there are three of them, these layers lend themselves well to a gold/silver/bronze analogy, so they collectively form what is often known as a medallion data architecture.

The distinction between 3NF in the silver layer and the Star Schema in the gold layer illustrates why different data model approaches are suitable for different stages of the data pipeline. While 3NF is efficient for organizing and integrating data, a Star Schema is optimized for user queries and reporting.

Define 3NF

Third Normal Form (3NF) is a data modeling approach that helps achieve data integrity and minimize redundancy within a relational database. To comply with 3NF, a database structure must follow certain technical rules: data must be stored in rows and columns, each table requires a unique identifier (primary key), column values must be atomic (containing simple, indivisible values), and relationships between tables are maintained via foreign keys.

In a multi-tier data architecture, 3NF typically occupies the middle or "silver" tier. It's the place where real data integration begins, consolidating data from various source systems into a unified format. The aim is to store data independently of source-specific and structural idiosyncrasies, with a focus on subject orientation - where all data related to a specific subject is integrated into a single schema.

Extract, Transform, and Load (ETL) pipelines are crucial for populating a 3NF layer. These pipelines encompass the embodiment of business rules needed for accurate data interpretation. Common ETL transformations include locating the accurate data sources for each part of the schema, converting data to adhere to the 3NF model’s structure and semantics, and ensuring that the data has the correct granularity and interpretations. Complex sources must be flattened and semistructured data must be rigorously transformed to fit into the 3NF schema.

Pros and cons of 3NF

Pros:

• Data Integration: Provides a consistent representation of how data actually is, facilitating comprehensive data integration.
• Minimized Redundancy: Ensures data is stored in the most efficient and unambiguous way, and that information density is maximized.
• Reliability: Adherence to technical rules ensures data consistency and reliability.

Cons:

• Rigidity: 3NF models have rigid structures, which make them less adaptable to capturing changes over time—both in data values and with respect to structure. Changes such as these, which happen over time, are known as Schema Drift.
• Complexity in Integration: Integration of semistructured data is not straightforward and may require significant transformation efforts.

In summary, 3NF models offer a structurally clean and efficient way to ensure data integrity and integration in the middle tier of a multi-tier data warehouse architecture, but they come with specific challenges in handling schema drift and integrating diverse data formats.

What is a Star Schema?

A Star Schema is a type of data model commonly used in the presentation (gold) tier of a multi-tier data architecture. Despite a common misconception, a Star Schema is not itself a data warehouse but a specific layer within it. Star Schema design prioritizes easy and targeted data consumption.

Data usually flows into the Star Schema layer after being refined and normalized in prior layers, including staging and integration. This final tier supports user interfaces and reporting mechanisms, leveraging Star Schema's clear and simple structure.

Typically, Extract, Transform, Load (ETL) pipelines transition data from more complex silver tier 3NF (or Data Vault) models into a Star Schema. Essential transformations ensure data joins are straightforward, often involving surrogate keys or sequences with lookups as left joins.

Physically loading a Star Schema requires first updating the dimension tables before the fact tables to ensure the lookups work correctly. One sign of a well designed 3NF (or Data Vault) model is when it is easy to create a virtual Star Schema as a layer of views.

Pros and cons of Star Schema

Pros:

• Simplified Queries: The star-shaped layout reduces the number of joins required, enabling faster query performance and simpler SQL statements.
• Clear Data Structure: Well-defined dimensions and facts facilitate intuitive understanding and use by end-users.
• Reusability: Reusing the same dimensions across multiple fact tables makes it easy to compare and correlate data, leading to the most insightful analyses.

Cons:

• Less Data Flexibility: While simplifying data analysis, Star Schemas are less flexible than normalized models due to their more rigid structure. Star Schemas are best for presentation: they are not a good choice for data integration.
• Increased Redundancy: The denormalization common in Star Schema leads to data redundancy, increasing the amount of storage space needed.

In summary, while a Star Schema offers many benefits for data analytics and reporting through its structured and efficient design, it does come with trade-offs, especially regarding data flexibility and storage. Nonetheless, when effectively implemented, a star schema layer is an invaluable part of any data warehouse.

Similarities and differences between 3NF and Star Schema

Both 3NF models and Star Schemas serve critical roles in a multi-layered data warehouse architecture. They represent the data from different perspectives and are suited for different tasks.

A 3NF model, used in the middle (silver) layer, captures real-world data structure and is foundational for data integration. Employing structured rules, 3NF reduces redundancy and enforces data integrity. However, its complexity and rigidity can make it less user-friendly for reporting purposes.

On the other hand, the Star Schema, used in the presentation (gold) layer, represents data from a perspective beneficial for analysis and reporting. Star Schemas organize information into facts and dimensions, which streamlines querying and correlation. Being less flexible, it specifically caters to ease of understanding and fast retrieval times for end users.

Summary

In summary, 3NF represents the data how it actually is: Star Schema represents the data how we would like to understand it.

While 3NF focuses on a detailed and normalized representation of data to minimize redundancy and enforce data consistency, a Star Schema prioritizes simplicity and speed of data retrieval, facilitating easy and efficient querying. Both paradigms complement each other, ensuring that a multi layered data warehouse architecture can support both robust data integration and efficient data analysis.

Further reading

• Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
• Building a Star Schema with Matillion
• Top 10 Tips for Creating Fact Tables Using Matillion
• Top 10 Tips for Creating Dimension Tables Using Matillion
• What to do with a Late Arriving Dimension or Early Arriving Facts
• Building a Type 2 Slowly Changing Dimension in Matillion's Data Productivity Cloud

Matillion is the data pipeline platform that empowers data teams to build and manage pipelines faster for AI and analytics - at scale, making it a stellar choice for constructing a multi-tier data warehouse. Start a 14-day trial for free.