Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Kafka ensures durability via: (1) Replication—configurable `replication.factor` (default 1, prod often 3). (2) Acks—`acks=all` waits for...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Fragma Data Systems. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Kafka ensures durability via: (1) Replication—configurable replication.factor (default 1, prod often 3). (2) Acks—acks=all waits for all in-sync replicas. (3) Persistence—messages written to disk, not just memory. (4) Consumer offsets—committed to __consumer_offsets for at-least-once. For reliability: Use min.insync.replicas=2; enable idempotent producer; use transactional producer for exactly-once. Configure retention.ms for replay. Best practice: Monitor lag, under-replicated partitions; use exactly-once semantics (Kafka 0.11+) for critical pipelines.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: acks=1 in production. Pro-Move: 'acks=all; min.insync.replicas=2; exactly-once.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.