OLTP systems handle operational transactions with integrity and low-latency requirements. OLAP systems perform large scans and aggregations. Heavy analytics on a transactional primary can compete for CPU, memory, and I/O, so teams often replicate or transform data into analytical systems.

Warehouses store curated analytical data; data lakes store diverse raw data; lakehouse approaches add transactional table metadata and governance on lake storage. Every platform needs schema evolution, lineage, data-quality checks, access controls, and freshness objectives.

Metric discrepancies can result from different definitions, event-time windows, duplicate handling, or late-arriving corrections. Define authoritative metric logic and publish it consistently across dashboards.