Understanding Data Vault on Wide Tables
Storing data as wide tables in a data lake while applying a logical Data Vault layer using views presents unique challenges. The goal is to virtualize a raw Data Vault model on top of a data lake while ensuring optimal performance.
In this article:
Key Considerations for Performance Optimization
- Data Remains the Same: Regardless of whether you use Iceberg, Snowflake, or another technology, the fundamental data characteristics remain unchanged. Issues like dirty data and transformations still apply.
- Descriptive vs. Transactional Data: Wide tables typically contain a mix of master and transactional data. Most attributes tend to be descriptive, especially in master data.
- Granularity Matters: Properly defining granularity helps structure the Data Vault model efficiently, especially for hubs, links, and satellites.
Virtualizing Data Vault on a Data Lake
When implementing a virtual Data Vault, consider:
- Hubs and Links: These should be materialized instead of virtualized. Business keys from wide tables should be extracted and stored in separate iceberg tables for efficiency.
- Satellites: Virtualizing satellites using views is recommended, but pay attention to GDPR and personal data separation.
- Indexing Performance: Hubs, links, PIT (Point-in-Time) tables, and bridges serve as indexes in a data lake environment.
GDPR and Data Privacy Challenges
One of the biggest concerns in a wide table approach is data privacy. Since wide tables often include personal data, you must consider logical deletion, encryption, or physical separation techniques to comply with regulations.
Enhancing Performance with Materialized Structures
To achieve good query performance, consider materializing certain structures:
- Materialized Hubs and Links: These structures act as indexes and improve data retrieval efficiency.
- PIT and Bridge Tables: These further optimize queries by structuring data in a way that minimizes computational load.
- Denormalized Information Marts: End-users should query fully materialized information marts, ensuring high-speed access.
Does Datavault4dbt Support This Approach?
Discussions around Datavault4dbt suggest it may support this approach in the future. If you’re working on a project with this implementation, consider reaching out to Scalefree to explore collaboration opportunities.
Conclusion
Applying Data Vault on wide tables within a data lake architecture requires careful planning. The key takeaways include:
- Virtualizing satellites while materializing hubs and links for performance.
- Addressing GDPR concerns by separating or encrypting personal data.
- Using PIT and bridge tables to enhance indexing and query speed.
- Building fully denormalized information marts for end-user access.
By following these best practices, organizations can ensure efficient and scalable Data Vault implementations on wide tables.
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Meet the Speaker
Michael Olschimke
Michael has more than 15 years of experience in Information Technology. During the last eight years he has specialized in Business Intelligence topics such as OLAP, Dimensional Modelling, and Data Mining. Challenge him with your questions!
