Is a data warehouse primarily optimized for operational transaction processing, or is it designed for analytical querying and historical analysis?

Introduction / Context:
Data warehouses are engineered to answer business questions quickly by scanning large volumes of historical data. They serve BI use cases such as trend analysis, cohort analysis, and KPI tracking—not point-of-sale inserts or bank transaction posting that characterize OLTP systems.

Given Data / Assumptions:

• Warehouse schemas (star/snowflake) emphasize read performance and simplicity for analysts.
• Columnar storage, partitioning, and compressed segments are common.
• Operational systems handle writes at high concurrency and enforce transactional integrity.

Concept / Approach:
Warehouses aggregate, denormalize, and index data to enable fast scans and aggregations. OLTP databases use normalized schemas, row-store layouts, and fine-grained locks to protect short transactions. Conflating the two leads to performance and maintenance issues.

Step-by-Step Solution:
Ingest data from OLTP via ETL/ELT into the warehouse.Model facts and dimensions for analytical readability.Optimize with partitioning, columnstores, and materialized summaries.Serve BI tools for aggregations and drill-through.

Verification / Alternative check:
Benchmark the same aggregation on OLTP vs. warehouse; the warehouse will typically complete faster due to columnar compression and star-join optimizations.

Why Other Options Are Wrong:
High-frequency transaction entry belongs to OLTP (option b). Unstructured ingestion (option c) is a data lake concern. Message queues (option d) are streaming/integration, not warehousing. Archival storage (option e) lacks the query acceleration features of a warehouse.

Common Pitfalls:
Running heavy reports against OLTP; over-normalizing warehouses; ignoring partitioning and summaries needed for scale.

Final Answer:
Designed for analytics and historical querying (not OLTP)