Data Vault Platform

Challenge

The client's existing data warehouse was struggling to keep pace with business needs. Reports took too long to generate, and adding new data sources required extensive rework of existing models.

Key pain points included:

• Long-running queries blocking business reporting
• Difficulty tracing data lineage for compliance
• Rigid schema that couldn't adapt to new requirements
• Source system changes causing downstream failures

Solution

1. Data Vault 2.0 Architecture

Redesigned the data warehouse using Data Vault methodology with three distinct layers:

• Raw Vault: Hubs, Links, and Satellites capturing business keys and relationships
• Business Vault: Computed attributes and business rules
• Information Mart: Dimensional models optimized for reporting

2. dbt Implementation

Built the entire transformation layer in dbt, leveraging:

• Modular SQL with reusable macros for Data Vault patterns
• Comprehensive testing for data quality
• Auto-generated documentation for data discovery
• Incremental models for efficient processing

3. Query Optimization

Implemented Snowflake-specific optimizations including:

• Clustering keys aligned with common query patterns
• Materialized views for frequently accessed aggregations
• Query tagging for cost attribution and monitoring

Data Model Highlights

Key entities modeled in the Data Vault:

• Customers: Hub with satellites for demographics, preferences, segments
• Products: Hub with satellites for attributes, pricing history, categories
• Transactions: Links connecting customers, products, stores, and time
• Inventory: Point-in-time satellites tracking stock levels

Results

The new architecture delivered significant improvements:

• Report generation time reduced by 60% for key dashboards
• New data sources can be integrated in days instead of weeks
• Complete audit trail for regulatory compliance
• Self-service analytics enabled for business users
• Cost-per-query reduced through better optimization