**At-most-once**: Producer fires and forgets. Messages may be lost. Use for metrics where loss is acceptable. **At-least-once**: Producer retries until ack; consumer may see duplicates. Simpler; use with idempotent consumer. **Exactly-once**: Transactional producer +...
This easy-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.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
At-most-once: Producer fires and forgets. Messages may be lost. Use for metrics where loss is acceptable.
At-least-once: Producer retries until ack; consumer may see duplicates. Simpler; use with idempotent consumer.
Exactly-once: Transactional producer + transactional consumer (Kafka 0.11+). Producer commits atomically; consumer reads-processes-commits in transaction. No duplicates, no loss.
Why It Matters: Financial transactions, billing, inventory need exactly-once. Analytics can tolerate at-least-once with idempotent sink.
Scalability Trade-offs: Exactly-once adds latency; transactional commits are slower. Consumer must support transactions.
Cost Implications: Exactly-once requires more careful design; failures and retries can increase cost. At-least-once + idempotent sink often sufficient.
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Analyze My Answer β FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data 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.