Is Data Vault worth it?

Data Vault has become an increasingly popular data warehousing architecture over the past decade. However, like any technology, it requires an investment of time, money and effort to implement. So is Data Vault really worth it? Let’s dig into the pros and cons to find out.

What is Data Vault?

Data Vault is a data warehousing architecture developed by Dan Linstedt in the 1990s. The key principles of Data Vault are:

• It is a hybrid approach between 3rd normal form (3NF) and dimensional modeling
• The architecture focuses on flexibility and scalability
• Data is stored in an immutable, traceable way
• Transformation of data is separate from storage

Some of the key components of a Data Vault implementation are:

• Hubs – These store business key information about core business entities
• Links – These record associations between hubs
• Satellites – These contain descriptive attributes about hubs and links
• Vaults – These are schema-less tables that contain hubs, links and satellites

By combining these components in a flexible way, Data Vault allows historical data to be stored without over-normalization, and new data sources can easily be added via new satellite tables.

Pros of Data Vault

Here are some of the main benefits that Data Vault offers:

Flexibility

A key advantage of Data Vault is flexibility in the data model. The loose coupling between hub, link and satellite tables means new data sources can easily be accommodated via additional satellites. These can be loaded without having to modify existing structures. The scalability of Data Vault models makes it easy to start small and expand organically as new data sources and requirements emerge.

Auditability

In Data Vault, data is loaded via an ETL process but is never updated or deleted. This means the full history of the data is retained, enabling detailed audit trails showing how data has changed over time. The architecture stores multiple versions of entities and tracks historical links via start/end dates. This level of traceability is hard to achieve in other data warehousing approaches.

Integration

Data Vault provides a robust foundation for enterprise data integration initiatives. By capturing data in standardized, reusable hubs and links, it becomes easier to consolidate data from disparate sources into a single version of the truth. The loosely coupled schema and staging area also facilitate incremental integration as new data sources are onboarded. Data Vault provides a flexible persistence layer for handling structured, semi-structured and unstructured data in one repository.

Load Performance

Data Vault models optimize load performance by staging incoming data in a raw vault, before distributing to hubs, links and satellites. Because loads are done in bulk into set-based structures, it enables high speed insertion from source systems. By separating the physical storage from the business model, Data Vault can accommodate frequent full loads from operational systems at high throughput.

Agility

Data Vault implementations facilitate agile delivery of data warehousing solutions. Because it does not require a lengthy, upfront normalization exercise, Data Vault projects can get up and running quickly. New business needs can be met via additional vaults, rather than having to remodel existing structures. This enables an agile, iterative approach as opposed to trying to design an entire data warehouse upfront.

Cons of Data Vault

However, there are also some potential downsides and challenges with Data Vault to be aware of:

Complexity

The Data Vault model is quite different from standard relational and dimensional approaches. For those used to working with star schemas and OLAP cubes, the hub/link/satellite structures can seem overly complex. It requires investment in training to understand how to design, build, utilize and maintain a Data Vault architecture. If expertise is lacking, it may be better to opt for simpler modeling approaches.

Resource Requirements

Because data is stored across a distributed network of hub, link and satellite tables, Data Vault requires more JOINS and indexing than a typical data warehouse design. This places additional resource demands on the database platform in terms of CPU, memory and storage. Data Vault may not scale well on lower spec database servers.

Query Performance

While Data Vault offers excellent load speed, the loose coupling of data across multiple tables can result in slower query performance. The many JOINS required to reunite attributes stored across vaults into consolidated views places demands on the query engine. Performance tuning and optimization of Data Vault designs is key. Query response times may not match simpler, denormalized schemas.

Scoping Effort

To fully realize the benefits of Data Vault, time and effort must be spent upfront determining the core business entities (hubs), relationships (links) and attributes (satellites) to include. While this scoping exercise is not as intensive as full data normalization, an investment is still needed to define the Data Vault model appropriately.

DBA Expertise Needed

Due to the resource demands and tuning required, dedicated DBA skills are necessary to build, manage and optimize a Data Vault data warehouse platform. The separation of loading from querying requires different strategies and configurations. You need DBAs knowledgeable in both data ingestion and database performance to run Data Vault successfully.

When is Data Vault suitable?

Given the pros and cons, Data Vault is most suitable in the following types of scenarios:

• When full historical data retention and auditability is required
• For enterprise data warehouse initiatives requiring incremental, agile delivery
• When source systems change frequently and flexibility is needed to accommodate
• For hybrid transaction/analytical processing (HTAP) requirements
• When unstructured or semi-structured data needs to be integrated with structured data
• For cloud-based data warehousing on scalable platforms like AWS

The following table summarizes where Data Vault is and isn’t optimal:

Should you use Data Vault?

So in summary, here are some guidelines on whether Data Vault is the right choice for your data warehousing needs:

• If you need to retain and audit historical data, then Data Vault offers robust capabilities here
• If you are looking for flexibility and agility to meet emerging requirements, Data Vault facilitates this well
• If you have variable source systems and data types, Data Vault provides a unifying model
• If you are building an enterprise, distributed data warehouse, Data Vault lays solid foundations
• If you are using the latest cloud data platforms, Data Vault can maximize scalability
• If you have limited data modeling experience, a simpler schema may be preferable
• If fast query performance is critical, Data Vault may not achieve the same speed as a star schema
• If you lack specialized Data Vault expertise, it will require investment in training

By examining your specific data, users, priorities and resources, you can determine whether Data Vault is the right fit or if an alternative data warehousing model is more appropriate.

Conclusion

Data Vault offers an agile, flexible enterprise data warehousing architecture, optimized for scalability and incremental delivery. For workloads that require handling diverse, rapidly changing data sources, traceability of historical data, and integration across structured and unstructured data, Data Vault can provide a robust platform.

However it does require specialized expertise to implement and manage successfully. The loose coupling of data across hubs, links and satellites can provide flexibility but at the cost of increased model complexity. Query performance may also suffer compared to simpler star schema designs.

By carefully weighing these pros and cons against your specific requirements, data profile and resources, you can determine if Data Vault is worth the investment for your organization. For the right use cases, Data Vault can provide future-proof scalability and integration as your data needs grow and evolve.