In an active (near real-time) data warehouse architecture, which components are typically present?

Introduction / Context:
An active data warehouse supports low-latency analytics, sometimes feeding operational decisions. Understanding what it includes clarifies how it differs from traditional batch-only warehouses.

Given Data / Assumptions:

• Modern warehouses integrate multiple sources (internal and external).
• They often present curated data marts for subject areas.
• They support near real-time or micro-batch ingestion to reduce data latency.

Concept / Approach:
Active architectures extend classic ETL to event streaming or frequent micro-batches. Data marts expose department-specific views. Source diversity (ERP, CRM, clickstream, third-party feeds) is standard. Therefore, all listed components are characteristic of active DWs.

Step-by-Step Solution:

Verification / Alternative check:
Vendor reference architectures show ingestion layers, streaming/micro-batch, curated layers, and marts/semantic models for BI.

Why Other Options Are Wrong:
Each single option captures only part of the architecture; the combined “All of the above” is most accurate.

Common Pitfalls:
Assuming active DWs are just OLTP; they remain analytical but reduce latency to minutes or seconds.

Final Answer:
All of the above.