Enterprise Data Vault Warehouse Implementation

Project Overview

Led the design and implementation of a comprehensive Data Vault data warehouse for $150bn asset management firm. The resulting database contained over 1 TB of operational data containing security master, trading, pricing, and accounting data. Final model consisted of over 100 tables and SSIS packages. Developed a system for automatically refreshing the vault after changes were made to the operational data.

Key Achievements

Scalability: Designed architecture to handle all data with ample room for growth
Performance: Achieved 99.9% data processing uptime
Agility: Reduced new data source integration time from weeks to days

Technical Implementation

Data Vault Architecture

The warehouse follows Data Vault methodology with three core components:

Hubs: Core business entities (Security, Holding, Trade)
Links: Business relationships and transactions
Satellites: Descriptive attributes and historical context

Technology Stack

Database: SQL Server 2017 with Always On Availability Groups
ETL: SSIS with custom Data Vault loading patterns
Orchestration: SSIS and SQL agent jobs for workflow management
Modeling: ER Studio for data modeling and documentation
Visualization: Power BI for executive dashboards

Performance Optimizations

Implemented use of system-versioned tables to capture slowly-changing values.
Created automated data quality checks using T-SQL
Designed parallel processing architecture for high-volume sources
Optimized query performance with strategic indexing

Business Impact

Data Integration: Reduced time to integrate new data sources
Data Quality: Achieved 99.8% data accuracy through automated validation
Reporting: Enabled real-time dashboards for executive decision-making

Lessons Learned

This project reinforced the importance of:

Proper Data Vault modeling for long-term maintainability
Automated testing in data pipeline development
Documentation for complex enterprise systems
Stakeholder communication throughout the implementation

Future Enhancements

Migration to cloud-native architecture
Implementation of real-time streaming capabilities
Advanced analytics and machine learning integration
Enhanced data governance and lineage tracking