Start by clarifying requirements: data volume, latency, consistency, and the number of cloud environments. Then propose a scalable, fault-tolerant architecture using a change data capture (CDC) pipeline with a message queue and cloud-specific publishers, and discuss trade-offs between push vs. pull and exactly-once vs. at-least-once semantics.
Pro tip: Emphasize idempotency and dead-letter queues to handle failures gracefully, and mention how you'd monitor data drift across clouds—this shows you think about production reliability, not just the happy path.
Ask about data volume, update frequency, latency tolerance, consistency needs, and the specific cloud environments. This ensures your design addresses the actual constraints.
Propose a pipeline: database → CDC (e.g., Debezium) → message queue (e.g., Kafka) → cloud-specific publishers. Explain how this decouples source and destinations for scalability.
Describe each component: how CDC captures changes, how the queue buffers and orders messages, and how publishers handle cloud-specific APIs, retries, and batching.
Discuss delivery semantics (at-least-once vs. exactly-once), idempotent writes, dead-letter queues, and monitoring for data consistency across clouds.
Compare push vs. pull, synchronous vs. asynchronous, and managed vs. self-hosted services. Explain how the design scales with data volume and number of clouds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.