In data warehousing and OLTP contexts, most operational (day-to-day) information systems are designed around transient data that is updated in place rather than kept as historical snapshots. Is this statement accurate?

Introduction / Context:
Operational systems (also called OLTP systems) support day-to-day business processes such as order entry, invoicing, and inventory adjustments. This question checks your understanding of the difference between transient data (updated in place) and periodic data (append-only history), and which one is typical for operational workloads.

Given Data / Assumptions:

• Operational systems prioritize fast inserts/updates for current state.
• Historical trend analysis is not the primary goal; it is typically handled elsewhere (data warehouses or data lakes).
• Terminology: “transient data” = update-in-place; “periodic data” = never physically altered once stored.

Concept / Approach:
In OLTP design, rows commonly represent the current truth. When a customer’s address changes, the same record is updated (transient behavior). This keeps tables smaller and queries fast for operational users. In contrast, analytics needs history; data warehouses record changes as additional rows (periodic behavior) to enable time-series reporting. Understanding where each style fits improves schema design and ETL planning.

Step-by-Step Solution:

Verification / Alternative check:
Inspect audit trails: if the production tables show a single “current” record per business key and change history is kept in separate audit/warehouse tables, the system uses transient data in the core OLTP layer.

Why Other Options Are Wrong:

• “Incorrect” conflicts with standard OLTP practice.
• “Applies only to data warehouses/analytical workloads” inverts the roles; warehouses favor periodic, not transient.
• “Depends exclusively on the file system” misunderstands that this is a modeling and workload choice, not a storage-format feature.

Common Pitfalls:
Expecting operational tables to hold long-term history, which bloats tables and slows transactions. Best practice is to offload history to analytic stores while keeping OLTP lean.

Final Answer:
Correct