3NF vs Data Vault

Data warehouses are complex systems designed to aggregate and analyze large volumes of data from multiple sources, in support of decision-making processes. 

In data warehousing, a well-planned data architecture is vital to ensure robustness, efficiency, and long term ease of maintenance. A multi-layered approach is often chosen as a way to tackle the many data warehousing challenges independently, and to ensure simpler maintenance and better scalability.

Typically, data warehouse architectures consist of at least three layers: an initial layer dedicated to data acquisition (bronze layer), a middle layer for data integration (silver layer), and a presentation layer aimed at making the data easily queryable (gold layer). This structured approach is often referred to as a medallion data architecture. Each layer employs different data models tailored to its specific function.

In the context of the middle layer, 3NF (Third Normal Form) and Data Vault are two prominent data modeling methodology choices. They play a pivotal role in ensuring the seamless integration and reliable transformation of raw data into a coherent, unified format.

Defining 3NF

Third Normal Form (3NF) is a foundational concept in database normalization. It is a data modeling technique where data is organized with minimal redundancy and optimal integrity. Data in 3NF conforms to various technical rules to prevent ambiguity and ensure consistency. These rules include the necessity of a primary key for unique identification, the requirement for atomic column values, and the use of foreign keys to define relationships.

In data warehousing, 3NF is typically employed in the middle or "silver" tier of a multi-tier data architecture. This middle layer bridges raw data storage and higher-level analytical structures, providing a single, consistent, integrated data representation. 3NF organizes data according to subject orientation, which means storing all data related to a single subject in one place. In this way 3NF minimizes redundancy and preserves the true state of the data, detached from the many disparate original source systems.

Populating a 3NF Layer using ETL Pipelines

Extract, Transform, Load (ETL) processes are critical for populating a 3NF layer. These pipelines handle various tasks including sourcing necessary data components, performing required semantic and structural transformations, and ensuring data granularity and consistency. The ETL operations embody business logic, making raw data meaningful and relationally structured within the 3NF model.

Pros and Cons of 3NF

The strengths of a 3NF data model lie in its rigid consistency and minimal redundancy, making it ideal for preserving data integrity and ensuring high information density. If designed correctly, every piece of data is stored once. Subject orientation is the key to data integration.

However, this rigidity is also a drawback. 3NF database structures do not easily accommodate changes in structure over time. They can also be vulnerable to changes in semantics, where data values change meaning. This makes 3NF less flexible compared to other models - like Data Vault - that are better suited for rapidly evolving data landscapes.

In summary, while 3NF models offer reliability and clarity for consistent data integration, their static nature can be a significant limitation in a highly dynamic business.

What is Data Vault?

Data Vault is a data modeling methodology designed for long-term historical storage of data originating from multiple operational systems. Data Vault models adhere to the rules of 3NF, but go further by categorizing all tables into three entity types: Hubs, Links, and Satellites.

• Hubs store business keys. This tremendously powerful concept ensures that the data warehouse is always relevant to the business processes. It also underpins subject orientation.
• Links store relationships between Hubs. All relationships are modeled as many-to-many. Any changes to the fundamental relationships between entities are simple to represent accurately, with no need to refactor the design.
• Satellites store descriptive attributes and context. Schema drift is easily handled by just adding new satellite tables. Some architects argue that semi-structured data is also an appropriate storage option here.

This separation simplifies data integration and allows for a very agile and maintainable data infrastructure.

In a multi-tier data architecture, a Data Vault model (or models) are commonly deployed somewhere between the staging (bronze) area and the presentation (gold) layer. Data from various source systems is first collected in staging tables, then pushed into the Data Vault layer(s) for long-term storage. Later, this data is transformed and moved to a dimensional model or data mart in the presentation layer, where business intelligence tools can easily access it.

Pros and cons of Data Vault

Pros:

• Scalability: Easily accommodates growing data volumes and changing business processes.
• Flexibility: Rapid adaptability to new data sources without significant redesign.
• Auditability: All historical data is preserved, essential for compliance.

Cons:

• Complexity: The architecture can be intricate and requires significant upfront understanding and data modeling. It can be hard to read the data, with even simple queries requiring many joins. ETL processes tend to be highly technical, and usually benefit from automation
• Storage: Can consume more storage compared to traditional models due to the large number of tables that are needed.
• Delayed Integration: Data Vault's highly flexible structure means that it can accommodate ambiguous data with no difficulty. The consumer must interpret the data, which makes downstream ETL more complex.

Data Vault provides a robust and efficient framework for modern data warehousing, particularly suited for organizations requiring flexibility and historical accuracy. Though its complexity can be a challenge, the benefits of adaptability and scalability often outweigh the drawbacks.

Comparing Third Normal Form (3NF) and Data Vault methodologies

Developing a good Data Vault model requires an excellent understanding of the business as it actually is. This is costly up front but becomes more and more valuable over time.

The best 3NF models represent the business as it is supposed to work. In practice - as data architects and engineers know - there is always a difference between theory and reality.

• 3NF deals with this discrepancy in the ETL that is required to transform the supplied data so it fits the target model. Interpretation of a 3NF model is structure-driven.
• Data Vault deals with this discrepancy by being highly flexible in what is allowed to be stored. In other words, the interpretation of a Data Vault model is data-driven.

Both 3NF and Data Vault aim to ensure data integrity and consistency. 3NF accomplishes these things through strict adherence to normalization rules including primary and foreign keys. Data Vault goes further, requiring that all tables perform the role of either a Hub, a Link, or a Satellite. This allows Data Vault to maintain both structural integrity and historical accuracy.

Both methodologies are typically used in layered architectures. 3NF is usually deployed as a single middle (silver) tier, whereas Data Vault can be deployed as multiple layers between the staging (bronze) and presentation (gold) layers.

Data integration requires that data is integrated by subject. 3NF and Data Vault are both perfect for this, and consequently are often used as the single source of truth in a data warehouse. 3NF relies on a data model clearly defined up front. In contrast Data Vault categorizes Hubs based on business keys, and assumes that all relationships are many-to-many.

In a 3NF model, all tables are highly structured, consistency is rigidly enforced with minimal data redundancy. It's less adaptable to change, but the ETL processes needed to create the downstream presentation layer (such as a star schema) are relatively simple.

In contrast, Data Vault models are highly adaptable and scalable, designed to handle changing data landscapes and integrate new data sources rapidly. This is to some extent a postponement of data integration. The ETL processes to create the presentation layer need to deal with ambiguity and interpretation, which makes them potentially more complex, with higher maintenance costs.

In terms of size, 3NF models tend to be simpler than the equivalent Data Vault model, requiring less than half the number of tables overall.

Summary

The 3NF model is well-suited for stable data environments requiring data integrity with minimal redundancy. In contrast, the Data Vault methodology provides robust, scalable, and adaptable solutions for dynamic data landscapes needing comprehensive historical storage and easy integration of new data sources. This comes at the cost of added complexity and storage overhead.

The static nature of 3NF makes it less suitable for dynamic business environments. 3NF offers reliable, consistent data but is less focused on historical data and lineage.

Further reading

• Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
• Declarative Data Vault 2.0 in Snowflake with Matillion ETL
• VaultSpeed and Matillion - get productive with Data Vault
• Data Vault 2.0 Pipelines for the Matillion Data Productivity Cloud
• Data Vault 2.0 Shared Jobs for Matillion ETL

Matillion is the data pipeline platform that empowers data teams to build and manage pipelines faster for AI and Analytics - at scale. Combining productivity, collaboration, speed, and a code-optional approach, Matillion is an excellent choice for implementing a 3NF or Data Vault layer in a data warehouse. Its intuitive UI with pre-built components and the option to code in SQL, Python, or DBT caters to diverse skill sets, making it flexible enough for the most sophisticated data modeling. Start a 14 day trial for free.