---
title: "Data Warehouse Types: A Complete Guide to Architectures and Use Cases"
description: "A data warehouse is a centralized repository for structured data, supporting complex queries, reporting, and business intelligence rather than transaction processing. The guide compares architectures by scale, latency, cost, scope, ownership, and governance. Enterprise data warehouses integrate organization-wide sources through ETL, apply cleansing and validation, and provide a governed source of truth, while data marts focus on departmental analysis and may be dependent or independent. Operational Data Stores replicate current or recent operational data for reporting refreshed from minutes to hours, whereas virtual, cloud, hybrid, and lakehouse designs trade physical consolidation, scalability, flexibility, and governance differently. The comparison also frames lakehouses as combining open-format data lake flexibility with warehouse-style governance and transactional reliability."
---

# Data Warehouse Types: A Complete Guide to Architectures and Use Cases

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Jun 18, 2026

**Type:** Explainer

## Summary

A data warehouse is a centralized repository for structured data, supporting complex queries, reporting, and business intelligence rather than transaction processing. The guide compares architectures by scale, latency, cost, scope, ownership, and governance. Enterprise data warehouses integrate organization-wide sources through ETL, apply cleansing and validation, and provide a governed source of truth, while data marts focus on departmental analysis and may be dependent or independent. Operational Data Stores replicate current or recent operational data for reporting refreshed from minutes to hours, whereas virtual, cloud, hybrid, and lakehouse designs trade physical consolidation, scalability, flexibility, and governance differently. The comparison also frames lakehouses as combining open-format data lake flexibility with warehouse-style governance and transactional reliability.

## Context

Organizations need to choose among data warehouse architectures before selecting a platform or planning a migration. The choice involves tradeoffs among scale, latency, cost, subject scope, ownership, data quality, governance, workload requirements, and team structure.

## Approach / What changed

The guide classifies Enterprise Data Warehouses, data marts, Operational Data Stores, virtual warehouses, cloud warehouses, hybrid warehouses, and lakehouses, then describes their architectures, refresh patterns, governance characteristics, scalability, and suitable analytical or operational use cases.

## Takeaways

- An Enterprise Data Warehouse integrates data across business units through ETL, cleansing, validation, consistent business rules, and access controls to provide an organization-wide source of truth.
- A dependent data mart inherits governance and data quality from an existing EDW, while an independent mart is faster to build but can create conflicting business rules and reporting silos.
- An Operational Data Store supports current-state reporting with data refreshed from minutes to hours, while the EDW remains intended for historical trends, strategic analysis, and complex multidimensional queries.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Analytics](https://yomu.fyi/topic/data-analytics), [Data Pipelines](https://yomu.fyi/topic/data-pipelines)

- Source: [Databricks](https://www.databricks.com/blog/data-warehouse-types)
- Source URL: https://www.databricks.com/blog/data-warehouse-types
- Ingested by Yomu: 2026-08-30T17:01:00.590Z

[Read original post](https://www.databricks.com/blog/data-warehouse-types)