DATA ARCHITECTURE THAT DOCUMENTS ITSELF

Quick Answer: NUUN Digital designs and builds data architectures that scale with the business — dimensional modelling, data vault, one-big-table, or hybrid approaches, with dbt-based transformation pipelines and auto-generated documentation. Architecture that analysts, engineers, and AI systems all can use.

WHAT WE DELIVER

Data architecture strategy. Warehouse, lake, lakehouse, or hybrid approach.
Dimensional and vault modelling. Star schema, data vault 2.0, or anchor modelling as appropriate.
dbt transformation layer. Source → staging → intermediate → marts with tests and documentation.
Schema and naming conventions. Governance for consistent, discoverable data.
Semantic layer integration. Bridge between warehouse and BI/AI consumers.
Auto-generated documentation. dbt docs, Dataedo, or Atlan integrations.

HOW WE DO IT

Assess current state. Data sources, consumption patterns, and pain points.
Choose the modelling approach. Based on update patterns, query patterns, and team skills.
Design the architecture. Layered transformation with clear separation of concerns.
Build with tests from day one. dbt tests catch data-quality issues at transformation time.
Document automatically. Lineage and metadata generated from the code, not maintained by humans.

WHEN IT FITS

Organizations scaling beyond ad-hoc SQL to shared, governed models.
Data-warehouse migrations (on-prem to cloud, warehouse to lakehouse).
Post-acquisition data-stack consolidation.
AI/ML projects requiring clean, documented training data.

SELECTED WORK

Enterprise client — dbt-based transformation layer → model run time [X]× faster; documentation coverage [X]%. Read case →
SaaS client — Dimensional model rebuild → analyst productivity up [X]%, metric disputes down. Read case →

SOURCES & FURTHER READING

Data Management practice
ETL/ELT pipelines
Data governance and quality
dbt — https://www.getdbt.com/
Ralph Kimball dimensional modeling — https://www.kimballgroup.com/

Frequently asked.

Star schema or data vault?

Depends on update patterns and source complexity. Star schema for analytics-primary use cases with stable sources; data vault for volatile source systems with audit requirements; hybrid for most enterprise situations.

Do you use dbt exclusively?

Primarily. dbt has become the de-facto standard for transformation in modern stacks. For enterprises with specific constraints, alternatives (Dataform, SQLMesh, Azure Data Factory) are options.

What about data lakes vs. warehouses?

Lakehouses (Databricks, Iceberg-based architectures) are increasingly the default for organizations with both SQL and ML/AI workloads. Warehouses (Snowflake, BigQuery) remain strong for SQL-primary use. We recommend per workload.

How do you handle data quality?

Tests in the transformation layer (dbt tests), contract-based enforcement between teams, and monitoring of data freshness, completeness, and anomalies. Quality is a build-time concern, not just a runtime concern.

Can you work with existing data teams?

Yes, and usually preferred. Data-architecture engagements that transfer to in-house teams succeed at higher rates than replacement-style engagements.