top of page
Search

Snowflake: A Scalable Data Platform for Data Vault

  • Writer: Rhys Hanscombe
    Rhys Hanscombe
  • Dec 3, 2019
  • 2 min read

In December 2019, Dmytro Yaroshenko presented a webinar on how Snowflake’s cloud data platform is an ideal foundation for agile, scalable Data Vault implementations. Here’s a summary of the key insights and practical strategies from that session.


Why Snowflake for Data Vault?

Snowflake is a cloud-native data platform that offers:

  • Complete SQL database functionality

  • Near-zero management (no infrastructure headaches)

  • Support for all data types and users

  • Pay-as-you-go pricing

  • Live data sharing and seamless integration with popular ETL/ELT and analytics tools


Its multi-cluster, shared data architecture enables organizations to scale processing power and concurrency on demand, making it a perfect match for the dynamic needs of Data Vault projects.


Key Features for Agile Data Warehousing

The webinar highlighted several Snowflake features that empower agile Data Vault development:

  • Scalability: Instantly scale compute resources for both processing performance and user concurrency.

  • Flexible Architecture: Design databases and schemas to fit any data model or environment (DEV, UAT, PROD, etc.).

  • Multi-layered Design: Easily implement Landing, Staging, Raw Data Vault, Business Data Vault, and Information Delivery layers within Snowflake.


Performance at Scale

Snowflake’s architecture allows for:

  • Efficient handling of massive data volumes (from gigabytes to petabytes)

  • Automatic scale-out and scale-down for workloads with many models or users

  • Parallel processing for faster Data Vault loads and transformations


Real-world demos showed Data Vault models running on Snowflake with millions of records and hundreds of models, all with impressive performance and minimal management overhead.


Data Integration and Automation

Snowflake supports both deterministic and non-deterministic key strategies for Data Vault:

  • Deterministic keys (e.g., MD5 hashes) allow for parallel processing of Hubs, Links, and Satellites.

  • Non-deterministic keys (e.g., integer keys) may require parent-child dependencies but offer faster access.


The platform also enables continuous data pipelines using features like Snowpipe, Streams, and Tasks for real-time ingestion and transformation.


Everything-as-Code and Agile SDLC

Snowflake’s “everything-as-code” approach supports:

  • Automated environment/database setup

  • Robust security (RBAC, FGAC, SSO, MFA, etc.)

  • Virtual warehouses for compute scaling

  • Continuous integration and deployment (CI/CD)

  • On-demand environments and automated testing


Features like Zero Copy Clone and Time Travel make it easy to manage, test, and recover data environments without performance impact.


Key Takeaways for Data Vault on Snowflake

  • Unmatched scalability and flexibility for Data Vault projects of any size

  • Seamless integration with modern data tools and pipelines

  • Automated, agile development with robust security and governance

  • Efficient management of complex, multi-layered data architectures


Conclusion

Combining Data Vault methodology with Snowflake’s cloud data platform delivers a powerful, scalable, and agile solution for modern data warehousing. Whether you’re building your first Data Vault or scaling to enterprise-level analytics, Snowflake provides the tools and performance you need.

For more resources and expert guidance, visit your Data Community.

bottom of page