In the era of data modernization, organizations face the challenge of managing vast volumes of data while ensuring data integrity, scalability, and agility. To address these complexities, a powerful data warehousing solution like the Snowflake Data Cloud, coupled with an effective data modeling approach such as the Data Vault architecture, can be a winning combination.
By leveraging Snowflake’s cloud-native architecture and the principles of the Data Vault model, organizations can unlock numerous benefits in terms of scalability, performance, data integrity, and collaborative data sharing.
In this blog, we will dive deep into the world of data warehousing and explore the advantages of combining Snowflake with the Data Vault architecture. We will also discover how Snowflake’s cloud-native design complements the core principles of the Data Vault, leading to a robust and efficient data management framework. Lastly, we will explore the key benefits that organizations can achieve by adopting this combination.
Whether you are embarking on a new data warehousing initiative or looking to optimize your existing data infrastructure, this blog will equip you with the knowledge to leverage the combined power of Snowflake and the Data Vault architecture effectively.
What is a Data Vault Architecture?
Created in the 1990s by a team at Lockheed Martin, Data Vault Modeling is a hybrid approach that combines traditional relational data warehouse models with newer big data architectures to build a data warehouse for enterprise-scale analytics. It is agile, scalable, no pre-modeling required, and well-suited for fluid designs.
Data Vaults are created with three core structures:
The table that represents a core business entity within a company
Contain a defined business key and a few mandated data vault fields
Business keys are required to be unique per row
Do not contain any context or details about the business entity
The table that defines a relationship between two or more Hubs
Contains no context or details about the business entity
One row represents the relationship between two entities with their unique business key
Tables containing all contextual information about entities
Each row contains a unique key, row entry date, and business key to link to the Hub for the entity
Other considerations of data vaults:
All tables are inserted only
New data is inserted into tables as new rows with a new unique key
Rows are never deleted.
A satellite with an is_deleted flag represents rows that are no longer viable.
Query performance is optimized.
Because unique identifiers are required for every table, few comparisons are needed when utilizing joins in queries.
Tables can be loaded in parallel because ingestion pipelines don’t need to wait for other surrogate keys to be created in the database.
Why Leverage Snowflake for Data Vault Architecture?
Snowflake is known to be a flexible data warehousing solution built entirely in the cloud. It’s because of this flexibility that it goes perfectly with a flexible Data Vault Architecture. Here are some of the key features of Snowflake and how they would make a Data Vault more efficient and easier to implement:
Built for the cloud, Snowflake takes advantage of the elasticity and scalability of cloud infrastructure to handle large volumes of data and concurrent user queries efficiently.
Because of the insert-only feature of Data Vaults, being able to handle large volumes of data is essential.
Separation of Storage and Compute
Queries on Data Vaults are normally simpler to execute because of the hash keys for each table. With the separation of storage and computing that Snowflake provides, costs can be saved on compute resources in a Data Vault architecture over other architectures.
Built-in Security and Data Governance
Snowflake prioritizes data security and provides robust security features, including encryption at rest and in transit, role-based access control (RBAC), two-factor authentication (2FA), and data masking. It also supports compliance with various industry regulations and standards.
Multi-Table Insert (MTI)
Snowflake’s SQL multi-table INSERT allows you to insert data into multiple target tables in a single SQL statement in parallel with a single data source.
MTI can be especially useful in a Data Vault is it drastically improves insert performance while still ensuring atomicity and consistency when loading data into the Data Vault in a single transaction. The data is inserted into all the related tables, maintaining the integrity of the Data Vault model.
To learn more about how Snowflake’s features go hand in hand with Data Vaults, be sure to check out his blog on the subject.
Benefits of Combining Data Vault and Snowflake
Combining the Data Vault architecture with Snowflake data warehousing offers several benefits. Leveraging the strengths of both approaches will produce several key advantages, including:
Flexibility and adaptability for evolving business requirements
Data Vaults in Snowflake are built to evolve with changing requirements, as adding new Hubs, Links, and/or Satellites doesn’t disrupt other tables in the architecture.
With insert-only tables, changes to data are a simple, fast process of simply inserting new rows with a newly created date.
Simplified data integration and agility in data modeling
Snowflake offers seamless integration with various data integration tools and platforms. This integration capability complements the Data Vault architecture, simplifying data ingestion, transformation, and loading processes. It allows for efficient data integration from diverse sources into the Data Vault model, enhancing the agility of data integration workflows.
Incremental loading and historical data tracking capabilities
With the virtually unlimited storage capacity in Snowflake, a Data Vault can retain all historical data along with the created dates to track data as it evolves to be used in data science models
Enhanced scalability and performance through parallel processing
Snowflake’s multi-cluster shared data architecture enables high concurrency, which is essential for Data Vault workloads involving multiple users and complex data transformations. It allows concurrent access to the Data Vault tables without compromising performance, ensuring timely and efficient data processing.
Real-World Use Cases of Data Vault Architecture in Snowflake
There are many scenarios in which utilizing Data Vault architecture in Snowflake can benefit your business. Here are a few examples:
Regulatory Compliance and Audit Trail
Industries with strict regulatory requirements, such as finance, healthcare, and government, can benefit from Data Vault’s inherent data lineage and historical tracking capabilities. The architecture allows organizations to maintain an audit trail of data changes, supporting compliance audits, traceability, and regulatory reporting.
Supply Chain Management
Data Vault architecture can be applied in supply chain management scenarios, where data needs to be collected and integrated from multiple systems, including inventory management, logistics, procurement, and supplier data. The Data Vault model’s scalability and ability to handle complex relationships allow organizations to optimize their supply chain operations and gain insights for demand forecasting, inventory management, and supply chain analytics.
Fraud Detection and Risk Management
Organizations in industries prone to fraud, such as banking, insurance, and e-commerce, can leverage Data Vault to establish a comprehensive data foundation for fraud detection and risk management. By integrating data from various sources, including transaction records, user activity logs, and external risk indicators, the Data Vault architecture enables advanced analytics and anomaly detection to mitigate fraud and assess risk.
Best Practices for Implementing Data Vault in Snowflake
Implement a Robust Data Lineage Process: Data lineage is the process of tracking the flow of data through the data warehouse, from its source to its destination. Using dbt is one of the best choices.
Establish Naming Conventions and Standards: Establish naming conventions and standards for Data Vault modeling objects, such as hubs, links, and satellites.
Implement a Solid Data Governance Strategy: Data vault modeling can result in large, complex data structures; hence a solid governance model must be implemented from inception.
Hub and Link Simplification: Keep hubs and links as simple as possible, focusing on representing the core business entities and relationships. Avoid adding unnecessary complexity, as it can impact maintainability and query performance. Complex business rules and hierarchies are better suited for satellites.
Consistent Business Keys: Use consistent and stable business keys for your hubs and links. Consistency ensures that related records can be properly linked, facilitating data integration and accurate analysis. Avoid using system-generated keys for business-critical entities.
In conclusion, the combination of Snowflake’s cloud-native architecture and the Data Vault model presents a powerful solution for modern data management challenges. By harnessing Snowflake’s scalability and performance in conjunction with the flexibility and historical tracking of the Data Vault architecture, organizations can achieve a unified and robust data ecosystem.
Ready to start building your data warehouse with a data vault architecture in Snowflake? Be sure to start by reading our blog here to help you get started, or contact our team of experts!
Yes, it is possible to combine Data Vault with other modeling techniques, like dimensional modeling, to address specific reporting or analytical needs. However, it is crucial to ensure data integrity and consistency between the different modeling approaches.
Yes, various automation tools and frameworks can assist in generating Data Vault artifacts, including the creation of tables, views, and transformation scripts. These tools can streamline the development and maintenance of a Data Vault in Snowflake.