Design a data warehouse schema to track orders, customers, delivery partners, and payments.

Section 1 — The Context (The 'Why')
E-commerce schemas must unify orders, customers, delivery partners, and payments. The primary challenge is asynchronous updates and referential integrity when events arrive out of order. Updating fact rows loses audit trail.

Section 2 — The Diagram

[Order/Payment/Delivery Events] --> [Kafka] --> [Fact Tables]
[Customers/Partners] --> [SCD Dim] ----> [Conformed Schema]
                                              v
                                    [Redshift/Snowflake]

Section 3 — Component Logic
Event streams land in Kafka. Each fact table captures its grain. Conformed dimensions use SCD Type 2. Idempotency via event_id prevents duplicates from retries. Partitioning by order_date and customer_id. Exactly-once semantics ensure payment totals match order totals. 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)

Section 5 — Pro-Tip