Data Vault Optimization for a Global Food Ingredients Manufacturer

Challenge

A global food ingredients manufacturer with approximately 800 employees had introduced Data Vault as its data warehouse architecture. However, the initial implementation mixed several approaches: source-driven Data Vault elements without clear alignment to business processes, dimensional elements tailored too narrowly to specific reports, and a lack of proper hard rules for source data cleansing. The result was an unnecessarily complex model that blurred the boundaries between data integration and data architecture.

An architecture review revealed the consequences: the model had grown to over 3,500 elements in the Datavault Builder, excessive use of soft rules compensated for the missing hard rules, and inconsistent modeling standards made the data model opaque. Developers faced long processing times, high error rates when extending the model for new requirements, and poor maintainability. The Exasol analytics database underperformed due to unfavorable data structures generated by the overly complex model.

Approach

Alligator Company conducted a comprehensive restructuring of the data architecture and modeling practices. The starting point was a series of Ensemble Logical Modeling (ELM) workshops with business stakeholders. These workshops aligned the Data Vault model with the actual business process language and requirements of domain experts, replacing the previous source-driven approach with a business-oriented model.

With the target model defined, Alligator Company established a clear separation of transformation responsibilities along proven Divide & Conquer principles. Hard rules now handle source data cleansing, Data Vault handles integration, and soft rules handle business logic. Data from sites running identical ERP systems (HR, orders, accounting, logistics) was combined and standardized through hard rules before entering the Data Vault layer.

To manage these transformations, the team introduced dbt for both hard rules and soft rules. Metadata-driven code generation in dbt-core reduced manual coding effort. Automated tests integrated into the CI pipeline raised pipeline quality and produced documentation as code.

The team also created dbt model proxies for Datavault Builder model elements. This established end-to-end data lineage across the entire pipeline, from source through Raw Vault to the delivery layer. Users gained full transparency into data origins, and root cause analysis for data quality issues became significantly faster.

In parallel, Alligator Company implemented DataOps practices: disposable development environments with automated CI pipelines enabled developers to work independently on different parts of the model. The previously manual deployment process was automated, making releases faster and more reliable. Improved monitoring provided better visibility into data pipeline runtimes.

Outcome

The restructured Data Vault model now reflects actual business processes, is significantly easier to maintain, and covers a broader scope of business requirements than the original. Through the ELM workshops, the model speaks the language of domain experts, and the simplified data structures improved Exasol database performance noticeably.

The internal team can now implement new or changed requirements in the data integration independently and make data accessible to business users without external support.

• Model complexity reduced by 84%: from 3,500 Datavault Builder elements to 550
• Broader business requirement coverage despite the simpler model
• Exasol database performance improved through configuration tuning and cleaner data structures