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Given ever-growing amounts of data and the challenges posed by data-intensive applications, implementing a modern data modeling approach has become important for all enterprises. A well-thought out data modeling strategy helps with faster time-to-market and improved application performance. It prevents architectural complexities, enables optimized resource management, and helps meet regulatory compliance.

Some of the top data modeling techniques used by data and analytics (D&A) teams include hierarchical, relational, entity-relationship, object-oriented, dimensional and data vault.

Data vault is the latest data modeling method used to build a scalable modern data platform. It’s an open source, hybrid model that uses database normalization principles and is based on dimensional modeling—the de facto technique for most current D&A applications. It addresses all the drawbacks of dimensional modeling and is designed to be flexible, scalable, consistent, and adaptable to the needs of any organization.

Data vault overview and advantages

Data vault modeling supports leading industry databases like Snowflake, Azure Synapse Analytics, Redshift, Oracle, and more. The biggest advantages of using data vault modeling is that it can be built and implemented incrementally, is technology agnostic, stores all historical data, and has flexibility to change—thus saving time and reengineering efforts.

Some of the main benefits of using data vault over conventional data modeling techniques are:

  • Flexibility – Dimensional modeling is not very flexible with respect to data source changes or granularity. Data vault accommodates changes with minimum or zero impact.
  • Complexity – Data vault is standardized and is very easy to use. All other data modeling approaches become more complex to use with an increasing number of data sources.
  • Volume – Data vault is suitable for a large-scale data warehouse.
  • Variety – It easily incorporates big data and NoSQL.
  • Velocity – Dimensional modeling is not very performant to handle live and parallel loading of some tables. Data vault supports such parallel loading.
  • Security – It’s easy to model secured data with data vault and it’s compliant with various regulatory needs. (In dimensional modeling, added efforts are needed to ensure security and compliance.)

Is data vault modeling right for you?

Data vault is designed for organizations looking to implement a highly scalable and agile data warehouse. It supports near real-time and parallel data loading, integration with multiple data sources, and better automation possibilities—all being main drivers for organizations to opt for this modeling technique. But data vault modeling can be complex to implement, especially for organizations lacking the right D&A skills and knowledge.

So, if your analytics requirements are small or even medium-sized, implementing data vault modeling might not be needed for your business case since it does present a longer learning curve. It works best for larger analytics requirements, where organizations have huge datasets and integrations with various data sources.


Authored By:
Yogesh Rajak Yogesh Rajak Senior Cloud Data Architect, InfoCepts

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