Within ETL, what does the “extract” process typically capture from operational systems?

Introduction / Context:
ETL (Extract, Transform, Load) feeds the data warehouse with curated data. Understanding “extract” clarifies scope and performance expectations in pipelines.

Given Data / Assumptions:

• Operational systems contain transactional, current-state data.
• Warehouses require only data relevant to analytics and compliance.
• Full extractions are rare due to volume, cost, and privacy constraints.

Concept / Approach:
Extraction commonly filters to specific tables, columns, and time windows (e.g., last N days), sometimes incrementally via CDC (change data capture). It is aimed at operational sources, not decision-support systems (which are downstream.

Step-by-Step Solution:

Verification / Alternative check:
ETL patterns (full load, incremental load, CDC) all emphasize extracting only necessary data for efficiency and compliance.

Why Other Options Are Wrong:
All data: Impractical and unnecessary.
Decision support systems: Not the typical extraction source; they are targets or consumers.

Common Pitfalls:
Pursuing “land everything” strategies that bloat storage and slow processing without added value.

Final Answer:
A subset of data from various operational systems