Enterprise needs for data architectures can be highly fluctuating and that’s why Data Vault modeling, with its flexibility to adapt to changing enterprise needs, is proving to be a favored option for many organizations. Where traditional data models would require extensive governance and testing, the Data Vault architecture makes data warehouses highly agile and enables fast implementation of evolving data relationships.
In this article for Inside BIGDATA, Moshe Kranc, chief technology officer, reveals the significance of Data Vault modeling and its key benefits and limitations that can help organizations make an informed choice on whether it meets their specific data architecture requirements.
“Data Vault architectures is an innovative, hybrid approach that combines the best of 3rd Normal Form (3NF) and dimension modeling. This data modeling technique enables historical storage of data, integration of data from different operational systems, and tracing of the origin of all the data coming into the database,” notes Moshe.