Design a database schema to store customer transactions, including attributes like region, product category, and timestamp.

Section 1 — The Context (The 'Why')
Customer transaction schemas must support multi-dimensional analysis (region, product, time). The primary challenge is balancing normalization with denormalization. Over-normalizing creates too many joins and kills query performance.

Section 2 — The Diagram

[Transactions] --> [Staging] --> [Fact]
[Customers/Regions/Products] --> [Dim] --> [Star Schema]

Section 3 — Component Logic
Transactions land in staging; fact holds amount, keys to dimensions. Customer includes region_id; SCD Type 2 for address changes. Partitioning by transaction_date and region_id. Idempotency via transaction_id. TTL for raw staging after load. In production, monitor consumer lag, checkpoint success rate, and sink write latency as primary SLOs. Partitioning strategies should align with query patterns; bucketing within partitions mitigates join skew. TTL policies on raw and intermediate data control storage cost while preserving replay capability for debugging and backfill. Data skew mitigation via salting or secondary hashing prevents single partitions from becoming bottlenecks. Exactly-once semantics require transactional commits at the sink; at-least-once delivery demands idempotent write logic to avoid duplicates. Fan-out patterns allow one source topic to feed multiple downstream consumers without re-ingestion. Backpressure handling ensures that slow processors do not cause unbounded buffer growth; Kafka consumer lag is a key metric. Schema evolution should follow additive-only rules where possible to avoid breaking consumer compatibility. The CAP trade-off should be documented per component: analytics typically favors AP, while financial reconciliation requires CP. Blast radius from component failure is bounded by replication and checkpointing; design for graceful degradation during partial outages. Cost optimization: use Spot instances for batch workloads and tier cold data to lower storage classes. Dead-letter queues preserve failed records for replay rather than dropping them.

Section 4 — The Trade-offs (The 'Senior' part)