**Decision framework**: Throughput, latency, replay, operational model, ecosystem. **Kafka (MSK)**: Higher throughput, partition replay, Kafka ecosystem (Connect, Streams). Use when you need exactly-once semantics, consumer lag monitoring, or multi-sink patterns. **Apache...
This medium-level Cloud/Tools question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Decision framework: Throughput, latency, replay, operational model, ecosystem. Kafka (MSK): Higher throughput, partition replay, Kafka ecosystem (Connect, Streams). Use when you need exactly-once semantics, consumer lag monitoring, or multi-sink patterns. Apache Pulsar: Multi-tenancy, tiered storage, geo-replication. Use for multi-tenant SaaS. Google Pub/Sub: Serverless, at-least-once. Simpler ops; no partition management. Azure Event Hubs: Kafka-compatible API; good Azure integration. Trade-offs: Kinesis is AWS-native, 24-hour retention by default. Kafka offers flexibility, longer retention, replay. Pub/Sub is simplest for GCP. Cost: Kinesis $0.015/shard/hour; MSK has broker cost; Pub/Sub is per-message. At 1M msg/sec, costs diverge significantly. Best practice: Choose by cloud and team expertise; Kafka if you need replay or complex topologies.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.