The Data Vault Handbook - Concepts and Applications

for a single, integrated version of the truth, while other methods favored a bottom-up, star schema approach that prioritized ease of reporting and analysis.

However, these methods encountered limitations, particularly when facing the growing complexity and rapid changes of modern data environments.

LIMITATIONS OF TRADITIONAL MODELING METHODS

— Handling Big Data and High Velocity: Can struggle to keep pace with the volume and speed of big data sources, leading to performance issues and delays — Managing Data Source Changes: Require substantial rework when data sources evolve, making them less adaptable to the dynamic nature of today’s data landscapes — Maintaining Flexibility in Business Rules: Embed business rules within the model, meaning any rule change can require extensive restructuring, which is costly and time- consuming — Ensuring Data Lineage and Auditability: With an increased need for data governance, traceability, and regulatory compliance, traditional models may lack built-in mechanisms for maintaining full history and audit trails of all changes in the data