Is Data Vault still relevant?

Data Vault is a data modeling approach that was first introduced in the early 2000s by Dan Linstedt. It aimed to provide a more flexible and scalable approach to data warehousing compared to traditional modeling techniques. At its core, Data Vault focuses on a hub-and-spoke model that links business concepts together using relationships rather than consolidating data into denormalized structures.

In the two decades since its inception, Data Vault has seen widespread adoption by organizations looking to build enterprise data warehouses and enable business intelligence. However, with the rise of new big data technologies and approaches like data lakes, some have questioned whether Data Vault remains a relevant technique today.

In this article, we’ll explore the key principles and benefits of Data Vault modeling and assess its applicability for modern data architectures. The key questions we’ll address are:

Is Data Vault scalable for large volumes of data?

Does Data Vault enable flexibility for new data sources and schemas?

Can Data Vault be used with new data platforms like Hadoop and cloud data warehouses?

Does Data Vault still meet the needs of business users compared to alternatives?

By the end of this article, you should have a clear perspective on whether Data Vault remains a viable data modeling approach or if other techniques are better suited for current needs.

Key Principles of Data Vault

To understand if Data Vault is still relevant, we first need to recap some of its core principles and distinguishing characteristics:

• Hub-and-spoke model – The fundamental pattern in Data Vault is modeling business entities as hubs that are connected to various attributes and events using links and satellites. This retains context while avoiding dependency on physical models.
• Normalized structure – Data is stored in a highly normalized way, avoiding redundancy and potential inconsistencies. Joins are used to recreate integrated views at query time.
• Immutable storage – Records in a Data Vault are never updated or deleted (“append-only”). This maintains an accurate history for auditing/lineage.
• Hash keys – Each hub, link, and satellite has a hash-based primary key that does not expose or imply intelligence.
• Integration events – Links capture events that associate hubs together and enable temporal analysis (e.g. a Customer placed an Order).
• Separation of concerns – Load workflows, transformations, and persistent storage are separated to isolate changes.

These core concepts enable Data Vault to achieve scalability, flexibility, and resilience – but they also introduce complexity compared to standard data modeling approaches. As we’ll explore next, this tradeoff informs whether Data Vault remains optimal for modern needs.

Scalability

One of the main benefits ascribed to Data Vault is its ability to scale smoothly to handle extremely large volumes of data. This scalability stems from its use of normalized structures, hash keys, and separation of processing workflows.

To assess Data Vault’s scalability merits today, we need to consider contemporary benchmarks and architectures:

• Cloud data warehouses like Snowflake and BigQuery now enable companies to store petabytes of data extremely efficiently.
• Hadoop-based data lakes build on distributed storage and processing to cost-effectively handle web-scale big data.
• Low-cost object stores like Amazon S3 can store virtually unlimited amounts of raw data which can be processed on-demand.

As this comparison shows, Data Vault can scale to handle very large data volumes into the multi-petabyte range. However, it can require more optimization and tuning relative to the distributed storage and processing of Hadoop data lakes. And it likely won’t match the scalability of cloud data warehouses that leverage MPP architectures and effectively unlimited storage.

That said, Data Vault’s principles still provide valuable guidance for scaling datasets larger than traditional data warehouses can handle. So it remains broadly relevant for “big data” needs, even if not as turnkey as cloud or Hadoop-native solutions.

Flexibility

A core benefit of Data Vault’s hub-and-spoke model is that it decouples physical data representations from the consistent business entities being modeled. This is designed to provide flexibility as source systems change over time.

To evaluate Data Vault’s ongoing flexibility merits, we need to compare it against alternatives like:

• Data lakes – which store raw data “as is” in schemaless repositories like Hadoop HDFS or S3.
• Schema on read – designing schemas at query time based on the analyst’s needs.
• Agile modeling – iteratively adjusting models via techniques like continuous integration.

Data Vault does provide more flexibility than traditional conformed dimensions and entity-relationship modeling. By isolating changes to satellites and links, it enables incremental updates without refactoring core models.

However, Data Vault ultimately still depends on predefined hub schemas and relationships that require rework if business entities change significantly. This limits flexibility compared to the “no model” approach of data lakes and schema on read. Data lakes also better support ad hoc schemas that differ across analytics use cases.

On the other hand, Data Vault provides more robust core structure than a pure data lake. And it can be evolved incrementally using agile modeling techniques. So for flexibility, Data Vault occupies a middle ground between highly structured and fully schemaless environments.

New Platform Integration

When assessing Data Vault relevance, we also need to consider how effectively it integrates with contemporary data platforms like Hadoop and cloud data warehouses.

Key points of comparison:

• Data Vault originated in traditional RDBMS systems like Teradata and Oracle, which differ substantially from big data platforms.
• Hash keys and hub-and-spoke joins may require optimization on columnar and distributed systems.
• Denormalization and star schemas are common for analytics performance in new platforms.
• MPP cloud data warehouses utilize partitioning, clustering, materialized views to improve query performance.

In practice, we are now seeing Data Vault deployed across a range of modern platforms:

• Azure SQL Data Warehouse supports large Data Vault implementations.
• Snowflake’s micro-partitioning helps manage Data Vault’s high normalization.
• Data Vaults built on Hadoop often leverage Hive for management and Presto for query performance.

So leading platforms have shown Data Vault can be architected to work well. However, it may require more optimization relative to simpler models. Data lakes may also be better suited than Data Vault to leveraging Hadoop’s scalability and schema flexibility. But Data Vault can still integrate effectively in a modern data stack alongside other architectures.

Meeting Business Needs

Ultimately, the relevance of any data approach comes down to its ability to meet the use cases and needs of business users and decision-makers.

To assess Data Vault in this regard, we need to evaluate how well it enables:

• Integration across varied data sources.
• Support for enterprise reporting and dashboards.
• Analytics for both standard and ad hoc business questions.
• Low latency performance for high-value workloads.
• Data lineage and auditability.

Data Vault’s normalized hub-and-spoke model facilitates integration across heterogeneous sources. And it can be queried to populate dimensional models that feed BI tools and dashboards.

For high performance analytics, Data Vault may require optimization and indexing. Data lakes built directly on storage like HDFS can analyze raw data faster. And cloud data warehouses offer very performant SQL analytics.

On the governance side, Data Vault provides detailed change tracking and time-stamping to support audits and lineage. Its separation of raw data from transformations also improves governance.

So in summary, Data Vault can absolutely support downstream business intelligence and analytics. It requires more investment than a simple data lake, but provides better structure and governance. The ultimate choice depends on the priorities, use cases, and culture of the organization.

Conclusion

In conclusion, while Data Vault originated in a very different technical era, the core principles and ideas largely remain relevant for modern data architectures. Data Vault provides a robust, scalable way to model enterprise data that balances flexibility and governance.

Key positives that sustain Data Vault’s relevance:

• Ability to handle large data volumes in the petabyte range.
• Flexibility to adapt to source system changes.
• Integration with major cloud and Hadoop platforms.
• Auditability and lineage capabilities.

Potential limitations to consider:

• Need for optimization on some platforms over simpler models.
• Less ad hoc flexibility than pure data lakes.
• More complex than standard dimensional modeling.

The suitability of Data Vault depends on several factors – data volumes, rate of change, the need for governance, and capability of the implementation team. It can be a great choice for mid-to-large enterprises with complex data landscapes and a need for robust data warehousing. For newer companies operating primarily in the cloud at start-up scale, Data Vault may be overkill compared to simpler modeling approaches.

By weighing these considerations against organizational needs, technology leaders can make an informed choice about whether to adopt Data Vault as a core part of their data architecture. While not always the optimal choice, Data Vault remains a viable and relevant option – especially for large enterprises operating hybrid transactional/analytical processing environments.