Data Vault Archives - Zorost Intelligence | AI, Cloud & Data Experts https://zorost.com/tag/data-vault/ Production AI systems for aviation, manufacturing, pharma, government, finance, freight, and geopolitical intelligence. Wed, 20 May 2026 18:52:40 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://zorost.com/wp-content/uploads/2025/08/ZOROST-Intel-Logo3_512-150x150.png Data Vault Archives - Zorost Intelligence | AI, Cloud & Data Experts https://zorost.com/tag/data-vault/ 32 32 81719879 Dimensional Modeling on Delta Lake — and When to Choose Data Vault Instead https://zorost.com/dimensional-modeling-delta-data-vault/ Tue, 09 Dec 2025 09:00:00 +0000 https://zorost.com/dimensional-modeling-delta-data-vault/ Star schema, data vault, one-big-table, federation. Each has a different shape and different trade-offs on Delta Lake. A decision framework.

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Pull-quote: “There is no single right model. There is the right model for the workload.”

Why this matters

Dimensional modeling is a forty-year-old discipline. Lakehouse architecture is a five-year-old discipline. Most teams import their old habits into the new platform and produce models that work but underperform — or models that look modern but break under load.

The right approach is workload-driven.

Four patterns to choose from

Pattern When to use Strengths Weaknesses
Star schema Reporting and dashboards dominate; Power BI / Tableau is the primary consumer Familiar; BI-tool friendly; fast slicing on Photon-enabled Delta Less agile to change; many-to-many requires bridge tables
Data Vault 2.0 Many sources; auditability is required; the model needs to evolve continuously Auditable; agile; handles many sources; clear separation of business keys, satellites, and links More tables; queries usually need a presentation layer
One Big Table API-driven sub-second queries dominate; consumers are applications, not analysts Sub-second queries; simple semantics for app developers Joins move into ETL; updates can be expensive
Lakehouse Federation Cross-system reporting without governance ownership No data movement; fast to deliver Performance depends on source; governance has to be explicit

Decision tree

Primary consumer of the model?
   ├── Analysts / BI tools  ──► Star schema (consider Direct Lake)
   ├── Apps / APIs          ──► One Big Table or Star with caching
   ├── Many sources, audit  ──► Data Vault 2.0
   └── Cross-system reporting, no copy possible ──► Lakehouse Federation

How we structure the medallion architecture

Regardless of model pattern, we maintain a Bronze/Silver/Gold separation:

Layer Purpose Typical retention
Bronze Raw + arrival timestamp + source ID; immutable Long (years)
Silver Parsed, conformed, deduplicated; data quality enforced Medium (months to years)
Gold Business-ready aggregates / dimensions / facts Short to medium

The model pattern (star, vault, OBT) lives in Gold.

When to mix

Mixing is normal. A typical enterprise customer ends up with:

  • Data Vault 2.0 for the foundational integration of multiple sources
  • Star schema in Gold for analytical consumers
  • One Big Table in Gold for app consumers
  • Lakehouse Federation for occasional cross-system reporting

Closing

Dimensional modeling on Delta Lake is dimensional modeling, with new physics. Photon, liquid clustering, and Z-order are the storage primitives that change query performance economics. The choice of model still depends on the workload — but the trade-offs are different now than they were a decade ago.

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