Architecting the Future of Enterprise Data

Results and Benefits The transformation yielded significant improvements, showcasing the tangible business value of the new Data Fabric: • Development Speed and Automation: The use of dbt macros and the DataVault4dbt • Organizational Agility: Adopting a Data Mesh-like approach

package allowed the team to generate code for almost the entire Silver layer

decentralized data ownership, empowering business units to manage their own data products without becoming bottlenecks. This shift, supported by the agile Kanban methodology, fostered a collaborative environment that adapted quickly to evolving business requirements. The successful migration to a cloud-based Data Fabric marked a significant milestone for SpareBank 1 Sør-Norge. The partnership with Scalefree proved invaluable, providing the expert guidance and tailored solutions necessary to implement Data Vault effectively. By establishing dbt as the engine for their data transformation, the bank not only improved data quality and performance but also gained a strategic advantage. They are now positioned for continued growth, with a flexible platform capable of leveraging future data-driven insights and AI initiatives.

automatically. This eliminated technical debt and refactored inefficiencies, making the codebase more maintainable and extensible compared to the legacy SQL Server solution. • Cost Efficiency and Optimization: The bank achieved significant cost control by shutting down dynamic development and testing environments when not in use. Furthermore, the Gold layer was completely virtualized where possible. By avoiding physical data movement for the final presentation layer, the bank saved on storage costs and reduced processing time. • Scalability and Performance: Migrating to Snowflake provided a separation of storage and computing, ensuring that reporting and loading processes no longer competed for resources. An insert-only strategy was adopted to leverage Snowflake’s analytical strengths, resulting in faster data processing and improved query performance. • Improved Data Quality and Trust: dbt’s testing framework was integral to the migration. Automated standard and metadata tests ensured that migrated data matched the source, guaranteeing accuracy. The dbt data catalog provided end-users with a “menu” of data descriptions, owners, and granularity, significantly enhancing trust and self-service capabilities.

DATA VAULT WITH DBT TRAINING Harnessing the power of a modern data platform to build a flexible and future-proof data pipeline with Data Vault and dbt.

-Introduction-

-Core Elements-

-Testing-

-Deployment & Orchestration-

-Leveraging Templates-

-TurboVault4dbt-

-Data Vault 2.1-

-DataVault4dbt-

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ARCHITECTING THE FUTURE OF ENTERPRISE DATA